Biostatistics

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https://en.wikipedia.org/wiki/Biostatistics

Biostatistics (sometimes referred to as biometry) is a branch of statistics that applies statistical methods to a wide range of topics in the biological sciences, with a focus on clinical medicine and public health applications. The field encompasses the design of experiments, the collection and analysis of experimental and observational data, and the interpretation of the results. It is closely related to medical statistics.

History

Biostatistics and genetics

Biostatistical modeling forms an important part of numerous modern biological theories. Genetics studies, since its beginning, used statistical concepts to understand observed experimental results. Some genetics scientists even contributed with statistical advances with the development of methods and tools. Gregor Mendel started the genetics studies investigating genetics segregation patterns in families of peas and used statistics to explain the collected data. In the early 1900s, after the rediscovery of Mendel's Mendelian inheritance work, there were gaps in understanding between genetics and evolutionary Darwinism. Francis Galton tried to expand Mendel's discoveries with human data and proposed a different model with fractions of the heredity coming from each ancestral composing an infinite series. He called this the theory of "Law of Ancestral Heredity". His ideas were strongly disagreed by William Bateson, who followed Mendel's conclusions, that genetic inheritance were exclusively from the parents, half from each of them. This led to a vigorous debate between the biometricians, who supported Galton's ideas, as Raphael Weldon, Arthur Dukinfield Darbishire and Karl Pearson, and Mendelians, who supported Bateson's (and Mendel's) ideas, such as Charles Davenport and Wilhelm Johannsen. Later, biometricians could not reproduce Galton conclusions in different experiments, and Mendel's ideas prevailed. By the 1930s, models built on statistical reasoning had helped to resolve these differences and to produce the neo-Darwinian modern evolutionary synthesis.

Solving these differences also allowed to define the concept of population genetics and brought together genetics and evolution. The three leading figures in the establishment of population genetics and this synthesis all relied on statistics and developed its use in biology.

• Ronald Fisher worked alongside statistician Betty Allan developing several basic statistical methods in support of his work studying the crop experiments at Rothamsted Research, published in Fisher's books Statistical Methods for Research Workers (1925) and The Genetical Theory of Natural Selection (1930), as well as Allan's scientific papers. Fisher went on to give many contributions to genetics and statistics. Some of them include the ANOVA, p-value concepts, Fisher's exact test and Fisher's equation for population dynamics. He is credited for the sentence "Natural selection is a mechanism for generating an exceedingly high degree of improbability".
• Sewall G. Wright developed F-statistics and methods of computing them and defined inbreeding coefficient.
• J. B. S. Haldane's book, The Causes of Evolution, reestablished natural selection as the premier mechanism of evolution by explaining it in terms of the mathematical consequences of Mendelian genetics. He also developed the theory of primordial soup.

These and other biostatisticians, mathematical biologists, and statistically inclined geneticists helped bring together evolutionary biology and genetics into a consistent, coherent whole that could begin to be quantitatively modeled.

In parallel to this overall development, the pioneering work of D'Arcy Thompson in On Growth and Form also helped to add quantitative discipline to biological study.

Despite the fundamental importance and frequent necessity of statistical reasoning, there may nonetheless have been a tendency among biologists to distrust or deprecate results which are not qualitatively apparent. One anecdote describes Thomas Hunt Morgan banning the Friden calculator from his department at Caltech, saying "Well, I am like a guy who is prospecting for gold along the banks of the Sacramento River in 1849. With a little intelligence, I can reach down and pick up big nuggets of gold. And as long as I can do that, I'm not going to let any people in my department waste scarce resources in placer mining."

Research planning

Any research in life sciences is proposed to answer a scientific question we might have. To answer this question with a high certainty, we need accurate results. The correct definition of the main hypothesis and the research plan will reduce errors while taking a decision in understanding a phenomenon. The research plan might include the research question, the hypothesis to be tested, the experimental design, data collection methods, data analysis perspectives and costs involved. It is essential to carry the study based on the three basic principles of experimental statistics: randomization, replication, and local control.

Research question

The research question will define the objective of a study. The research will be headed by the question, so it needs to be concise, at the same time it is focused on interesting and novel topics that may improve science and knowledge and that field. To define the way to ask the scientific question, an exhaustive literature review might be necessary. So the research can be useful to add value to the scientific community.

Hypothesis definition

Once the aim of the study is defined, the possible answers to the research question can be proposed, transforming this question into a hypothesis. The main propose is called null hypothesis (H₀) and is usually based on a permanent knowledge about the topic or an obvious occurrence of the phenomena, sustained by a deep literature review. We can say it is the standard expected answer for the data under the situation in test. In general, H_O assumes no association between treatments. On the other hand, the alternative hypothesis is the denial of H_O. It assumes some degree of association between the treatment and the outcome. Although, the hypothesis is sustained by question research and its expected and unexpected answers.

As an example, consider groups of similar animals (mice, for example) under two different diet systems. The research question would be: what is the best diet? In this case, H₀ would be that there is no difference between the two diets in mice metabolism (H₀: μ₁ = μ₂) and the alternative hypothesis would be that the diets have different effects over animals metabolism (H₁: μ₁ ≠ μ₂).

The hypothesis is defined by the researcher, according to his/her interests in answering the main question. Besides that, the alternative hypothesis can be more than one hypothesis. It can assume not only differences across observed parameters, but their degree of differences (i.e. higher or shorter).

Sampling

Usually, a study aims to understand an effect of a phenomenon over a population. In biology, a population is defined as all the individuals of a given species, in a specific area at a given time. In biostatistics, this concept is extended to a variety of collections possible of study. Although, in biostatistics, a population is not only the individuals, but the total of one specific component of their organisms, as the whole genome, or all the sperm cells, for animals, or the total leaf area, for a plant, for example.

It is not possible to take the measures from all the elements of a population. Because of that, the sampling process is very important for statistical inference. Sampling is defined as to randomly get a representative part of the entire population, to make posterior inferences about the population. So, the sample might catch the most variability across a population. The sample size is determined by several things, since the scope of the research to the resources available. In clinical research, the trial type, as inferiority, equivalence, and superiority is a key in determining sample size.

Experimental design

Experimental designs sustain those basic principles of experimental statistics. There are three basic experimental designs to randomly allocate treatments in all plots of the experiment. They are completely randomized design, randomized block design, and factorial designs. Treatments can be arranged in many ways inside the experiment. In agriculture, the correct experimental design is the root of a good study and the arrangement of treatments within the study is essential because environment largely affects the plots (plants, livestock, microorganisms). These main arrangements can be found in the literature under the names of "lattices", "incomplete blocks", "split plot", "augmented blocks", and many others. All of the designs might include control plots, determined by the researcher, to provide an error estimation during inference.

In clinical studies, the samples are usually smaller than in other biological studies, and in most cases, the environment effect can be controlled or measured. It is common to use randomized controlled clinical trials, where results are usually compared with observational study designs such as case–control or cohort.

Data collection

Data collection methods must be considered in research planning, because it highly influences the sample size and experimental design.

Data collection varies according to the type of data. For qualitative data, collection can be done with structured questionnaires or by observation, considering presence or intensity of disease, using score criterion to categorize levels of occurrence. For quantitative data, collection is done by measuring numerical information using instruments.

In agriculture and biology studies, yield data and its components can be obtained by metric measures. However, pest and disease injuries in plants are obtained by observation, considering score scales for levels of damage. Especially, in genetic studies, modern methods for data collection in field and laboratory should be considered, as high-throughput platforms for phenotyping and genotyping. These tools allow bigger experiments, while turn possible evaluate many plots in lower time than a human-based only method for data collection. Finally, all data collected of interest must be stored in an organized data frame for further analysis.

Analysis and data interpretation

Descriptive tools

Main article: Descriptive statistics

Data can be represented through tables or graphical representation, such as line charts, bar charts, histograms, scatter plot. Also, measures of central tendency and variability can be very useful to describe an overview of the data. Follow some examples:

Frequency tables

One type of table is the frequency table, which consists of data arranged in rows and columns, where the frequency is the number of occurrences or repetitions of data. Frequency can be:

Absolute: represents the number of times that a determined value appear;

$${\displaystyle N=f_1+f_2+f_3+...+f_n}$$

Relative: obtained by the division of the absolute frequency by the total number;

$${\displaystyle n_i=\frac{f_i}{N}}$$

In the next example, we have the number of genes in ten operons of the same organism.

Genes = {2,3,3,4,5,3,3,3,3,4}

Genes number	Absolute frequency	Relative frequency
1	0	0
2	1	0.1
3	6	0.6
4	2	0.2
5	1	0.1

Line graph

Figure A: Line graph example. The birth rate in Brazil (2010–2016); Figure B: Bar chart example. The birth rate in Brazil for the December months from 2010 to 2016; Figure C: Example of Box Plot: number of glycines in the proteome of eight different organisms (A-H); Figure D: Example of a scatter plot.

Line graphs represent the variation of a value over another metric, such as time. In general, values are represented in the vertical axis, while the time variation is represented in the horizontal axis.

Bar chart

A bar chart is a graph that shows categorical data as bars presenting heights (vertical bar) or widths (horizontal bar) proportional to represent values. Bar charts provide an image that could also be represented in a tabular format.

In the bar chart example, we have the birth rate in Brazil for the December months from 2010 to 2016. The sharp fall in December 2016 reflects the outbreak of Zika virus in the birth rate in Brazil.

Histograms

Example of a histogram.

The histogram (or frequency distribution) is a graphical representation of a dataset tabulated and divided into uniform or non-uniform classes. It was first introduced by Karl Pearson.

Scatter plot

A scatter plot is a mathematical diagram that uses Cartesian coordinates to display values of a dataset. A scatter plot shows the data as a set of points, each one presenting the value of one variable determining the position on the horizontal axis and another variable on the vertical axis. They are also called scatter graph, scatter chart, scattergram, or scatter diagram.

Mean

Main article: Mean

The arithmetic mean is the sum of a collection of values (${\displaystyle x_1+x_2+x_3+\cdots +x_n}$) divided by the number of items of this collection (${\displaystyle n}$).

${\displaystyle \overline{x}=\frac{1}{n}\left(\sum _{i=1}^n{x}_i\right)=\frac{x_1+x_2+\cdots +x_n}{n}}$

Median

Main article: Median

The median is the value in the middle of a dataset.

Mode

Main article: Mode (statistics)

The mode is the value of a set of data that appears most often.

Comparison among mean, median and mode
Values = { 2,3,3,3,3,3,4,4,11 }

Type	Example	Result
Mean	( 2 + 3 + 3 + 3 + 3 + 3 + 4 + 4 + 11 ) / 9	4
Median	2, 3, 3, 3, 3, 3, 4, 4, 11	3
Mode	2, 3, 3, 3, 3, 3, 4, 4, 11	3

Box plot

Box plot is a method for graphically depicting groups of numerical data. The maximum and minimum values are represented by the lines, and the interquartile range (IQR) represent 25–75% of the data. Outliers may be plotted as circles.

Correlation coefficients

Although correlations between two different kinds of data could be inferred by graphs, such as scatter plot, it is necessary validate this though numerical information. For this reason, correlation coefficients are required. They provide a numerical value that reflects the strength of an association.

Pearson correlation coefficient

Scatter diagram that demonstrates the Pearson correlation for different values of ρ.

Pearson correlation coefficient is a measure of association between two variables, X and Y. This coefficient, usually represented by ρ (rho) for the population and r for the sample, assumes values between −1 and 1, where ρ = 1 represents a perfect positive correlation, ρ = −1 represents a perfect negative correlation, and ρ = 0 is no linear correlation.

Inferential statistics

Main article: Statistical inference

It is used to make inferences about an unknown population, by estimation and/or hypothesis testing. In other words, it is desirable to obtain parameters to describe the population of interest, but since the data is limited, it is necessary to make use of a representative sample in order to estimate them. With that, it is possible to test previously defined hypotheses and apply the conclusions to the entire population. The standard error of the mean is a measure of variability that is crucial to do inferences.

• Hypothesis testing

Hypothesis testing is essential to make inferences about populations aiming to answer research questions, as settled in "Research planning" section. Authors defined four steps to be set:

1. The hypothesis to be tested: as stated earlier, we have to work with the definition of a null hypothesis (H₀), that is going to be tested, and an alternative hypothesis. But they must be defined before the experiment implementation.
2. Significance level and decision rule: A decision rule depends on the level of significance, or in other words, the acceptable error rate (α). It is easier to think that we define a critical value that determines the statistical significance when a test statistic is compared with it. So, α also has to be predefined before the experiment.
3. Experiment and statistical analysis: This is when the experiment is really implemented following the appropriate experimental design, data is collected and the more suitable statistical tests are evaluated.
4. Inference: Is made when the null hypothesis is rejected or not rejected, based on the evidence that the comparison of p-values and α brings. It is pointed that the failure to reject H₀ just means that there is not enough evidence to support its rejection, but not that this hypothesis is true.

• Confidence intervals

A confidence interval is a range of values that can contain the true real parameter value in given a certain level of confidence. The first step is to estimate the best-unbiased estimate of the population parameter. The upper value of the interval is obtained by the sum of this estimate with the multiplication between the standard error of the mean and the confidence level. The calculation of lower value is similar, but instead of a sum, a subtraction must be applied.

Statistical considerations

Power and statistical error

When testing a hypothesis, there are two types of statistic errors possible: Type I error and Type II error.

• The type I error or false positive is the incorrect rejection of a true null hypothesis
• The type II error or false negative is the failure to reject a false null hypothesis.

The significance level denoted by α is the type I error rate and should be chosen before performing the test. The type II error rate is denoted by β and statistical power of the test is 1 − β.

p-value

The p-value is the probability of obtaining results as extreme as or more extreme than those observed, assuming the null hypothesis (H₀) is true. It is also called the calculated probability. It is common to confuse the p-value with the significance level (α), but, the α is a predefined threshold for calling significant results. If p is less than α, the null hypothesis (H₀) is rejected.

Multiple testing

In multiple tests of the same hypothesis, the probability of the occurrence of false positives (familywise error rate) increase and a strategy is needed to account for this occurrence. This is commonly achieved by using a more stringent threshold to reject null hypotheses. The Bonferroni correction defines an acceptable global significance level, denoted by α* and each test is individually compared with a value of α = α*/m. This ensures that the familywise error rate in all m tests, is less than or equal to α*. When m is large, the Bonferroni correction may be overly conservative. An alternative to the Bonferroni correction is to control the false discovery rate (FDR). The FDR controls the expected proportion of the rejected null hypotheses (the so-called discoveries) that are false (incorrect rejections). This procedure ensures that, for independent tests, the false discovery rate is at most q*. Thus, the FDR is less conservative than the Bonferroni correction and have more power, at the cost of more false positives.

Mis-specification and robustness checks

The main hypothesis being tested (e.g., no association between treatments and outcomes) is often accompanied by other technical assumptions (e.g., about the form of the probability distribution of the outcomes) that are also part of the null hypothesis. When the technical assumptions are violated in practice, then the null may be frequently rejected even if the main hypothesis is true. Such rejections are said to be due to model mis-specification. Verifying whether the outcome of a statistical test does not change when the technical assumptions are slightly altered (so-called robustness checks) is the main way of combating mis-specification.

Model selection criteria

Model criteria selection will select or model that more approximate true model. The Akaike's Information Criterion (AIC) and The Bayesian Information Criterion (BIC) are examples of asymptotically efficient criteria.

Developments and big data

Recent developments have made a large impact on biostatistics. Two important changes have been the ability to collect data on a high-throughput scale, and the ability to perform much more complex analysis using computational techniques. This comes from the development in areas as sequencing technologies, Bioinformatics and Machine learning (Machine learning in bioinformatics).

Use in high-throughput data

New biomedical technologies like microarrays, next-generation sequencers (for genomics) and mass spectrometry (for proteomics) generate enormous amounts of data, allowing many tests to be performed simultaneously. Careful analysis with biostatistical methods is required to separate the signal from the noise. For example, a microarray could be used to measure many thousands of genes simultaneously, determining which of them have different expression in diseased cells compared to normal cells. However, only a fraction of genes will be differentially expressed.

Multicollinearity often occurs in high-throughput biostatistical settings. Due to high intercorrelation between the predictors (such as gene expression levels), the information of one predictor might be contained in another one. It could be that only 5% of the predictors are responsible for 90% of the variability of the response. In such a case, one could apply the biostatistical technique of dimension reduction (for example via principal component analysis). Classical statistical techniques like linear or logistic regression and linear discriminant analysis do not work well for high dimensional data (i.e. when the number of observations n is smaller than the number of features or predictors p: n < p). As a matter of fact, one can get quite high R²-values despite very low predictive power of the statistical model. These classical statistical techniques (esp. least squares linear regression) were developed for low dimensional data (i.e. where the number of observations n is much larger than the number of predictors p: n >> p). In cases of high dimensionality, one should always consider an independent validation test set and the corresponding residual sum of squares (RSS) and R² of the validation test set, not those of the training set.

Often, it is useful to pool information from multiple predictors together. For example, Gene Set Enrichment Analysis (GSEA) considers the perturbation of whole (functionally related) gene sets rather than of single genes. These gene sets might be known biochemical pathways or otherwise functionally related genes. The advantage of this approach is that it is more robust: It is more likely that a single gene is found to be falsely perturbed than it is that a whole pathway is falsely perturbed. Furthermore, one can integrate the accumulated knowledge about biochemical pathways (like the JAK-STAT signaling pathway) using this approach.

Bioinformatics advances in databases, data mining, and biological interpretation

The development of biological databases enables storage and management of biological data with the possibility of ensuring access for users around the world. They are useful for researchers depositing data, retrieve information and files (raw or processed) originated from other experiments or indexing scientific articles, as PubMed. Another possibility is search for the desired term (a gene, a protein, a disease, an organism, and so on) and check all results related to this search. There are databases dedicated to SNPs (dbSNP), the knowledge on genes characterization and their pathways (KEGG) and the description of gene function classifying it by cellular component, molecular function and biological process (Gene Ontology). In addition to databases that contain specific molecular information, there are others that are ample in the sense that they store information about an organism or group of organisms. As an example of a database directed towards just one organism, but that contains much data about it, is the Arabidopsis thaliana genetic and molecular database – TAIR. Phytozome, in turn, stores the assemblies and annotation files of dozen of plant genomes, also containing visualization and analysis tools. Moreover, there is an interconnection between some databases in the information exchange/sharing and a major initiative was the International Nucleotide Sequence Database Collaboration (INSDC) which relates data from DDBJ, EMBL-EBI, and NCBI.

Nowadays, increase in size and complexity of molecular datasets leads to use of powerful statistical methods provided by computer science algorithms which are developed by machine learning area. Therefore, data mining and machine learning allow detection of patterns in data with a complex structure, as biological ones, by using methods of supervised and unsupervised learning, regression, detection of clusters and association rule mining, among others. To indicate some of them, self-organizing maps and k-means are examples of cluster algorithms; neural networks implementation and support vector machines models are examples of common machine learning algorithms.

Collaborative work among molecular biologists, bioinformaticians, statisticians and computer scientists is important to perform an experiment correctly, going from planning, passing through data generation and analysis, and ending with biological interpretation of the results.

Use of computationally intensive methods

On the other hand, the advent of modern computer technology and relatively cheap computing resources have enabled computer-intensive biostatistical methods like bootstrapping and re-sampling methods.

In recent times, random forests have gained popularity as a method for performing statistical classification. Random forest techniques generate a panel of decision trees. Decision trees have the advantage that you can draw them and interpret them (even with a basic understanding of mathematics and statistics). Random Forests have thus been used for clinical decision support systems.^{[citation needed]}

Applications

Public health

Public health, including epidemiology, health services research, nutrition, environmental health and health care policy & management. In these medicine contents, it's important to consider the design and analysis of the clinical trials. As one example, there is the assessment of severity state of a patient with a prognosis of an outcome of a disease.

With new technologies and genetics knowledge, biostatistics are now also used for Systems medicine, which consists in a more personalized medicine. For this, is made an integration of data from different sources, including conventional patient data, clinico-pathological parameters, molecular and genetic data as well as data generated by additional new-omics technologies.

Quantitative genetics

The study of population genetics and statistical genetics in order to link variation in genotype with a variation in phenotype. In other words, it is desirable to discover the genetic basis of a measurable trait, a quantitative trait, that is under polygenic control. A genome region that is responsible for a continuous trait is called a quantitative trait locus (QTL). The study of QTLs become feasible by using molecular markers and measuring traits in populations, but their mapping needs the obtaining of a population from an experimental crossing, like an F2 or recombinant inbred strains/lines (RILs). To scan for QTLs regions in a genome, a gene map based on linkage have to be built. Some of the best-known QTL mapping algorithms are Interval Mapping, Composite Interval Mapping, and Multiple Interval Mapping.

However, QTL mapping resolution is impaired by the amount of recombination assayed, a problem for species in which it is difficult to obtain large offspring. Furthermore, allele diversity is restricted to individuals originated from contrasting parents, which limit studies of allele diversity when we have a panel of individuals representing a natural population. For this reason, the genome-wide association study was proposed in order to identify QTLs based on linkage disequilibrium, that is the non-random association between traits and molecular markers. It was leveraged by the development of high-throughput SNP genotyping.

In animal and plant breeding, the use of markers in selection aiming for breeding, mainly the molecular ones, collaborated to the development of marker-assisted selection. While QTL mapping is limited due resolution, GWAS does not have enough power when rare variants of small effect that are also influenced by environment. So, the concept of Genomic Selection (GS) arises in order to use all molecular markers in the selection and allow the prediction of the performance of candidates in this selection. The proposal is to genotype and phenotype a training population, develop a model that can obtain the genomic estimated breeding values (GEBVs) of individuals belonging to a genotype and but not phenotype population, called testing population. This kind of study could also include a validation population, thinking in the concept of cross-validation, in which the real phenotype results measured in this population are compared with the phenotype results based on the prediction, what used to check the accuracy of the model.

As a summary, some points about the application of quantitative genetics are:

• This has been used in agriculture to improve crops (Plant breeding) and livestock (Animal breeding).
• In biomedical research, this work can assist in finding candidates gene alleles that can cause or influence predisposition to diseases in human genetics

Expression data

Studies for differential expression of genes from RNA-Seq data, as for RT-qPCR and microarrays, demands comparison of conditions. The goal is to identify genes which have a significant change in abundance between different conditions. Then, experiments are designed appropriately, with replicates for each condition/treatment, randomization and blocking, when necessary. In RNA-Seq, the quantification of expression uses the information of mapped reads that are summarized in some genetic unit, as exons that are part of a gene sequence. As microarray results can be approximated by a normal distribution, RNA-Seq counts data are better explained by other distributions. The first used distribution was the Poisson one, but it underestimate the sample error, leading to false positives. Currently, biological variation is considered by methods that estimate a dispersion parameter of a negative binomial distribution. Generalized linear models are used to perform the tests for statistical significance and as the number of genes is high, multiple tests correction have to be considered. Some examples of other analysis on genomics data comes from microarray or proteomics experiments. Often concerning diseases or disease stages.

Other studies

• Ecology, ecological forecasting
• Biological sequence analysis
• Systems biology for gene network inference or pathways analysis.
• Clinical research and pharmaceutical development
• Population dynamics, especially in regards to fisheries science.
• Phylogenetics and evolution
• Pharmacodynamics
• Pharmacokinetics
• Neuroimaging

Tools

There are a lot of tools that can be used to do statistical analysis in biological data. Most of them are useful in other areas of knowledge, covering a large number of applications (alphabetical). Here are brief descriptions of some of them:

• ASReml: Another software developed by VSNi that can be used also in R environment as a package. It is developed to estimate variance components under a general linear mixed model using restricted maximum likelihood (REML). Models with fixed effects and random effects and nested or crossed ones are allowed. Gives the possibility to investigate different variance-covariance matrix structures.
• CycDesigN: A computer package developed by VSNi that helps the researchers create experimental designs and analyze data coming from a design present in one of three classes handled by CycDesigN. These classes are resolvable, non-resolvable, partially replicated and crossover designs. It includes less used designs the Latinized ones, as t-Latinized design.
• Orange: A programming interface for high-level data processing, data mining and data visualization. Include tools for gene expression and genomics.
• R: An open source environment and programming language dedicated to statistical computing and graphics. It is an implementation of S language maintained by CRAN. In addition to its functions to read data tables, take descriptive statistics, develop and evaluate models, its repository contains packages developed by researchers around the world. This allows the development of functions written to deal with the statistical analysis of data that comes from specific applications. In the case of Bioinformatics, for example, there are packages located in the main repository (CRAN) and in others, as Bioconductor. It is also possible to use packages under development that are shared in hosting-services as GitHub.
• SAS: A data analysis software widely used, going through universities, services and industry. Developed by a company with the same name (SAS Institute), it uses SAS language for programming.
• PLA 3.0: Is a biostatistical analysis software for regulated environments (e.g. drug testing) which supports Quantitative Response Assays (Parallel-Line, Parallel-Logistics, Slope-Ratio) and Dichotomous Assays (Quantal Response, Binary Assays). It also supports weighting methods for combination calculations and the automatic data aggregation of independent assay data.
• Weka: A Java software for machine learning and data mining, including tools and methods for visualization, clustering, regression, association rule, and classification. There are tools for cross-validation, bootstrapping and a module of algorithm comparison. Weka also can be run in other programming languages as Perl or R.
• Python (programming language) image analysis, deep-learning, machine-learning
• SQL databases
• NoSQL
• NumPy numerical python
• SciPy
• SageMath
• LAPACK linear algebra
• MATLAB
• Apache Hadoop
• Apache Spark
• Amazon Web Services
• MyCalPharm: A software for pharmacology experiments, including Statistics Workshop. There are ten main topics that make up the module, each addresses an important aspect of statistics used in biological experiments - Data Features, Distribution of Data, Summary Statistics, Inferential Statistics, Choosing a Test, Sample Size, t-Test and Wilcoxon Test, ANOVA (Analysis of Variance), Correlation and Regression and Chi-Square Test.

Scope and training programs

Almost all educational programmes in biostatistics are at postgraduate level. They are most often found in schools of public health, affiliated with schools of medicine, forestry, or agriculture, or as a focus of application in departments of statistics.

In the United States, where several universities have dedicated biostatistics departments, many other top-tier universities integrate biostatistics faculty into statistics or other departments, such as epidemiology. Thus, departments carrying the name "biostatistics" may exist under quite different structures. For instance, relatively new biostatistics departments have been founded with a focus on bioinformatics and computational biology, whereas older departments, typically affiliated with schools of public health, will have more traditional lines of research involving epidemiological studies and clinical trials as well as bioinformatics. In larger universities around the world, where both a statistics and a biostatistics department exist, the degree of integration between the two departments may range from the bare minimum to very close collaboration. In general, the difference between a statistics program and a biostatistics program is twofold: (i) statistics departments will often host theoretical/methodological research which are less common in biostatistics programs and (ii) statistics departments have lines of research that may include biomedical applications but also other areas such as industry (quality control), business and economics and biological areas other than medicine.

Theories of love

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Theories_of_love

Theories of love refers to a number of often-competing theories which seek to explain the phenomenon of love. Findings from a number of disciplines, including sociology and evolutionary biology, are incorporated and synthesized in order to explain the phenomenon.

The term can refer to several psychological and sociological theories:

• Attachment theory
• Biology of romantic love (evolutionary theory and neuroscience)
• Color wheel theory of love (based on the 1973 book The Colors of Love by John Lee)
• Passionate and companionate love theory (based on research by Elaine Hatfield)
• Filter theory
• Reward theory of attraction
• Rubin's scale of liking and love (based on research by Zick Rubin)
• Triangular theory of love
• Vulnerability and care theory of love
• The social constructionist approach to love (proposed by Anne Beall and Robert Sternberg)

Love

Love is a complex, ever-changing concept that has evolved over the course of time. Different societies, cultures, and eras have attached different values to the word and have different perspectives on the concept. In the 17th century, one's family would pick the person one was going to marry based on social class and economic status. In some cultures, girls are married by the age of fourteen or even younger. In traditional definitions of love, love has been compared to God because of the power it has over those who believe in it. Love has the ability to be the source of human happiness, a sense of worth, and a source of healing from hurt or suffering.

In the 18th century, romantic love expressed sensibility and authenticity as it stood for "the truth of feeling". Many people view love as the reason for living. Symbolic interaction theorists believe that shared meanings, orientations, and assumptions form the basic motives behind people's actions. Cultural norms regarding the experience of love vary so that the emphasis in relationships is on sexual attraction, romantic courtship, intimate friendship, or commitment.

Although love can be the motive for some people's actions and bring people joy, love can also bring us sadness. "Love does us no good if we love the wrong person." When people open their hearts and show their flaws, vulnerabilities, and weaknesses to the wrong person, it can result in heartbreak, then causing feelings of regret. So why do humans deal with such a complicated thing such as love? Humans "need to love and to be loved".

The four types of love described in philosophy include agape, phileo, storge, and eros. Agape is a type of unconditional love that is less common in society but more apparent between individuals and their god. Phileo is a love used to describe friendship between individuals. This love is commonly seen between friends in public, especially as displays of warm gestures. Storge is another type of love that is expressed through parenting. Eros is a romantic love that was a type of love forbidden in early society and is still forbidden in some societies today. These different types of love are expressed differently based on culture.

Culture

Love is expressed in a multitude of forms, dependent on location and societal norms. Expressions of love can include acts such as self-sacrifice, compromise, courting, kissing, sex, and physical contact. Different cultures have adopted different customs; for instance, in Japan, public displays of affection are discouraged, and individuals typically express adoration in private. In France, people show their love by holding hands, kissing, and initiating sexual relationships. The United States has a different perspective on love, with people going on dates, having casual sex, and are open to meeting new people on social media or dating apps. Customs in the U.S. are generally more liberal when compared to other parts of the world. Marital traditions are largely cultural as well.

Marriage customs

Marriage is a legally binding union of two individuals who have commit to each other as partners in a personal relationship. However, the definition of marriage varies widely by culture, region, and jurisdiction. In the 19th century, many marriages were enforced by the parents of the individuals to satisfy political or economic factors in families. In India, arranged marriages and dowries remain a current practice. Elopement, also known as love marriage, has increased substantially in some parts of India. Marriage in Japan is more liberal in relation to arranged marriage, initially beginning with courtship that would allow love to develop, then eventually lead to marriage. In the United States, marriage customs vary, dependent on the desires of the individuals family, the individuals cultural background, and societal pressures.

Societal factors

Factors such as gender, race, economic status, age, religion, education, and ethnicity can influence an individual's views on both marriage and love. Shared expectations of age-appropriate behavior can pressure an individual into marriage. Depictions of love in social media and film also influence and pressure individuals in relation to love, and can potentially impact the expectations what marriage and should look like. Young adults are predominantly influenced by unrealistic depictions of love, witnessed in film and social media. For example, The Notebook depicts love as a force that can conquer all, the idealisation of one's partner, and the idea of soulmates.

Necessity of love

Love allows people to attribute a sense of purpose for living. From the moment of birth, relationships are made: mother and child, father and child, grandparent and child, and the like. As people grow older and enter into schools, jobs, and get involved in their communities the number of relationships, they have grown, as does their ability to maintain these relationships. Love can have a powerful effect on the human body. Irving Singer wrote, "For a person in love ... life is never without meaning. A person's life is built the love between two people – their parents, the love they share for the friendships they make and eventually, the person they marry and have children of their own with. The feelings love brings: happiness, empathy, mutual respect, a sense of purpose, can lead to stronger motivation, less stress, a positive outlook on life, and hope.

Love allows humans to communicate through their emotions. To love effectively, one has to love themselves first: to love another person's flaws and quirks, one has to love their own flaws and quirks.

Humans are not the only species in the world that can feel love and its effects. Non-human animals can feel love as well, although it is less complex and less creative. Many animals feel emotions. When a dog wags its tail or licks its owner after being parted for a few hours, this is interpreted as happiness. When a person leaves for work in the morning and their dog cries at the window, it exhibits sadness. A growling dog who doesn't like it when someone touches its favorite toy is showing anger. Animals can feel love as well as other basic emotions humans feel. Dogs that grow up with siblings create strong bonds with their siblings. If their sibling dies, the dog can go into depression and refuse to eat.

Love holds a higher significance than many people might assume. For example, Abraham Maslow, an American psychologist, developed a theory called "Maslow's Hierarchy of Needs". In this hierarchy, Maslow presents the different levels of priorities and needs we have as human beings. Maslow has listed physiological needs as the first essential need of human beings. Following physiological needs is safety needs which include the innate need for security, health, jobs, work, etc. Finally in third comes the need for belonging. Maslow describes this need as love, affection, family, friends, and intimacy. Although Maslow lists belonging on the third tier of the hierarchy of needs, one may argue that the sense of belonging, along with love and affection, could be the foundation of the pyramid of needs. When we explore the possibilities and actions that people take for love, it is clear how powerful it can be. For example, parents who are willing to risk their lives or die in the place of their child would be putting their belonging needs over their physiological need of safety. The motivation to express and feel love may overpower any physiological need that humans have.

Types of love

Humans come across different types of love as they reach different levels of maturity in their life, such as the love a mother feels for their child, the love that involves the instant attraction to a person, and the love that comes from years of being together. The love humans share for their family and friends can be viewed as "slow love". This love is based on finding shared interests and lifestyles that connect people to each other. It is a love that can be carried out because of the common interests that bind them together. It is more of a mental attraction than a physical attraction. Visually, we make interpretations of love based on the way a person looks. "Harmonism" and "echoism" are the ways a face is constructed that make one physically attractive: the distance between the forehead and nose, the distance between the mouth and chin, how close the eyes are together, and the sweep of one's eyebrows. The biochemical level fluctuation of a person can also explain the question "Who We Love". People who have expressive traits, such as curiosity and liveliness, tend to be drawn to people who have similar personalities. People who are cautious and socially conforming are attracted to their same kind as well. However, people who are foremost with expressive traits of sex hormones tend to be enchanted by their opposite kinds. People with a relatively high testosterone hormone are analytical and tough-minded. They tend to choose people with a relatively high estrogen hormone who are empathetic and pro-social. Besides the biochemical level explanation, there are also a few other elements that affect people's choices of mates. Another factor that influences who people choose to love is timing. Love can happen when one least expects it. Furthermore, people more easily fall in love when they are emotionally aroused, especially in a hard and lonely time. This is because such a mental state is associated with arousal mechanisms in the brain and elevated levels of stress hormones, both of which increase the level of the romantic passion hormone: dopamine. Distance is another element that influences people's love choices: people tend to choose to fall in love with those close to them. Childhood experience also influences mate choices. By the teenage years, people gradually construct a catalog of aptitudes and mannerisms they are looking for in a mate. Subtle differences in their experiences shape romantic tastes. Physical looks matter as well. From an anthropological point of view, a male tends to choose a female with a visual sign of youth and beauty, which indicates her high oestrogen level and strong reproductive ability. However, a female with a more pragmatic and realistic goal, tends to choose a male with education, ambition, wealth, respect, status and masculine appearance.

Another type of love people come across in their life is sexual love. As an individual crosses over from a child to a teen to an adult, this type of love becomes more relevant in their life. According to Milligan, "Sexualized intimate love is delusional and requires an overestimation of the person we love." A sexual love is a misconception of the person's beauty, intelligence, or charm. This type of love can reveal a lot about the person who's feeling such strong passionate feelings. It gives more insight into the lover than it gives about the loved one. Sexual love is not love at first sight – it is basic human instinct and hormonal responses.

Attachment theory of love

Psychiatrist and psychologist John Bowlby was the first to develop the attachment theory of love in Western culture. It focuses on the relationships or attachments that form between people. It starts with attachments made in infancy, stating that it is important for children to have a relationship with their primary caregivers in order to experience normal development. Though the underlying concepts originated in Mary Ainsworth's Strange Situation research, Bowlby organized the concepts into a more comprehensive theory. There are three tenets of this theory:

1. The creation of bonds is an intrinsic need.
2. Emotions and fear need to be regulated to increase vitality.
3. Adaptiveness and growth need to be encouraged.

According to this theory, one person in the relationship uses the other person as a "secure base", exploring the world from this person and using them as a safe place to return to when stressed or experiencing perceived danger. Bowlby's theory was extended from infants to adults by Cindy Hazan and Phillip Shaver. There is a transition of this attachment from the parent to a peer in adulthood. It is thought that proximity-seeking behavior is the first thing needed for this transition to occur. Much like the attachment styles identified in infants, there were four attachment styles identified for adults. These styles are secure, anxious -preoccupied, dismissive-avoidant, and fearful-avoidant. These attachment theories can influence adults differently in their romantic lives.

Secure-attachment

Adults who have a secure attachment style will be good at conflict resolution, be flexible in their thinking, communicate effectively, not be manipulative, have no fear of being enmeshed, hold the belief that they can positively impact their relationship, and care for their partner in the way that they wish to be cared for. They understand that there are a multitude of potential partners that could fulfill their needs and, therefore, feel confident leaving a partner that does not meet their needs. Research suggests that only one partner with a secure attachment style is necessary for a relationship to function in a healthy manner.

Anxious-preoccupied attachment

Adults who have an anxious-preoccupied attachment style tend to become overly dependent on their partners. They typically have trust issues, lower self-esteem, and higher levels of worry in their relationships. It is believed that these individuals may not have been able to develop the necessary defenses against separation anxiety and this leads to an emotional reaction to the perceived threat of separation. This thought pattern can lead these individuals to self-sabotage, causing them to tend to go after partners with a dismissive-avoidant style.

Dismissive-avoidant attachment

Adults with a dismissive-avoidant attachment style want to be independent. This desire for independence can lead these individuals to avoid relationships. They often have a hard time trusting other people and also view themselves highly. Their high self-esteem is supported by overemphasizing their competency and achievements. It is thought that this attachment style stems from trying to avoid being rejected or truly having no interest in being close to other people.

Fearful-avoidant attachment

Lastly, adults with a fearful-avoidant attachment style are not sure how they feel about intimate relationships. They have conflicting feelings of wanting emotional intimacy and feeling uncomfortable with it. They have trouble trusting others. They often feel that they are unworthy of affection. They also tend to avoid intimacy, or at least do not seek it out.

Criticism

There are still areas of this theory that have not been explored, such as contextual attachment within relationships. There has also been criticism for this theory. This includes criticism over Bowlby's wording of "partial deprivation" to describe a relationship with a caregiver that is unsatisfying. Critics claim that this wording was too vague and allowed people to over-extend this to any issues within the parent-child relationship. Other criticism stems from Hilda Lewis’ research which was not able to show a connection between separation from the mother and behavior. There have also been some calls to remove attachment disorder from clinical psychology because some critics believe that there is no professional consensus on what "attachment" means and how it should be utilized in the clinical setting.

Vertical and horizontal structure of love

Vertical structure

Social psychologist, Philips Shaver, and colleagues found that attachment processes could be represented in a hierarchy. By collecting data about males' and females' cognition of "love", researchers used a prototype approach to investigate the concept of love. "Love" is a basic level that concept includes super-ordinate categories of emotions: affection, adoration, fondness, liking, attraction, caring, tenderness, compassion, arousal, desire, passion, and longing. Love contains large sub-clusters that designate generic forms of love: friendship, sibling relationship, marital relationship etc. Such as, "affection", similar to "companionate love" in social psychology field, is the term most strongly co-occurs with terms in its generic sub-cluster and not with other terms in other sub-cluster groups: "Affection" for example contrasts significantly with "passionate love", which belongs to the second large sub-cluster – "lust".

Horizontal structure

Love can also be examined along a horizontal dimension with a prototype approach. Psychologists Beverley, Fehr and James Russell designed and conducted six experiments to examine the concept of love horizontally: free listing of subtypes of love; rating the goodness of love examples; reaction time to verify love category memberships; the fuzzy border of love definition; the sustainability of the subcategory of love; love subcategory family resemblances. For example, Beverly Fehr and James Russell examined the concept of love by carrying out the fifth experiment, the Sustainability of the Subcategory. They selected 10 sentences that defined "love" written by one group of participants and 10 definitions of "love" from textbooks. They asked other groups of participants to judge how weird or natural those sentences sounded when the word "love" in those definitions was substituted by targeted sub-category terms. When a prototypical sub-type substituted, such as friendship, the sentence sounded subjectively natural. However, when a peripheral sub-type, such as infatuation, took the place of "love" in the definitions, it yielded subjectively peculiar results. "In sum, Fehr identified a set of features of love that appear to have a clear prototype structure in terms of some features being better and some being poorer exemplars of the concept of love, and this difference appears to affect other aspects of the way love-related phenomena are processed."

Later Arthur Aron and Lori Westbay expanded the underlying structure of love prototype of Fehr's research. To understand the way people deal with love-related information, Aron and Westbay examined the latent structure and individual differences within Fehr's subgroup structure with three validation tests. They concluded that people generally understand the concept of love centralizing around three dimensions (passion, intimacy and commitment) which correspond to Sternberg's triangular theory of love. An individual's prototype of love limits his or her experience of a relationship, but the degree of these three dimensions that the individual emphasizes on depends on circumstances of that relationship.

Make America Great Again

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Make_America_Great_Again

"Make America Great Again" (MAGA, US: /ˈmæɡə/) is an American political slogan most recently popularized by Donald Trump during his presidential campaigns in 2016, 2020, and 2024. "MAGA" is also used to refer to Trump's ideology, political base, or to an individual or group of individuals from within that base. The slogan became a pop culture phenomenon, seeing widespread use and spawning numerous variants in the arts, entertainment and politics, being used by both supporters and opponents of Trump's presidency and as the name of the super PAC Make America Great Again Inc.

Originally used by Ronald Reagan as a campaign slogan in his 1980 presidential campaign ("Let's Make America Great Again"), it was adopted by Trump in 2015. It has been described as a slogan representing American exceptionalism and promoting an idealized or romanticized American past. While some scholars, journalists, and commentators have called the slogan a loaded phrase representing racism, regarding it as dog-whistle politics and coded language that excludes certain groups, others regard the slogan as patriotic and positive.

History

Ronald Reagan

See also: Ronald Reagan 1980 presidential campaign

Ronald Reagan campaign button

"Let's make America great again" was famously used in Ronald Reagan's 1980 presidential campaign. At the time, the United States was suffering from a worsening economy at home marked by stagflation. Using the country's economic distress as a springboard for his campaign, Reagan used the slogan to stir a sense of patriotism among the electorate. During his acceptance speech at the 1980 Republican National Convention, Reagan said, "For those without job opportunities, we'll stimulate new opportunities, particularly in the inner cities where they live. For those who've abandoned hope, we'll restore hope and we'll welcome them into a great national crusade to make America great again."

Bill Clinton

See also: Bill Clinton 1992 presidential campaign

The phrase was used in speeches by Bill Clinton during his 1992 presidential campaign. President Clinton also used the phrase in a radio commercial aired for wife Hillary Clinton's 2008 presidential primary campaign. During the 2016 electoral campaign, in which his wife was running against Trump, he suggested that Trump's version, used as a campaign rallying cry, was a message to White Southerners that Trump was promising to "give you an economy you had 50 years ago, and ... move you back up on the social totem pole and other people down."

Use by Donald Trump

Further information: Donald Trump 2016 presidential campaign, Donald Trump 2020 presidential campaign, Donald Trump 2024 presidential campaign, and Trumpism

In December 2011, following speculation that he would challenge sitting president Barack Obama in the 2012 United States presidential election, Trump released a statement in which he said he was unwilling to rule out running as a presidential candidate in the future, explaining "I must leave all of my options open because, above all else, we must make America great again." In December 2011, he also published a book using as a subtitle the similar phrase "Making America #1 Again", which in a 2015 reissue was changed to "Make America Great Again!" On January 1, 2012, a group of Trump supporters filed paperwork with the Texas secretary of state's office to create the "Make America Great Again Party", which would have allowed Trump to be that party's nominee if he had decided to become a third-party candidate in the presidential election.

Trump began using the slogan formally on November 7, 2012, the day after Barack Obama won his re-election against Mitt Romney. Trump used the slogan in an August 2013 interview with Jonathan Karl. By his own account, he first considered "We Will Make America Great", but did not feel like it had the right "ring" to it. "Make America Great" was his next slogan idea, but upon further reflection, he felt that it was a slight to America because it implied that America was never great. He eventually selected the phrase "Make America Great Again", later claiming that he was unaware of Reagan's use in 1980 until 2015, but noted that "he didn't trademark it." On November 12, he signed an application with the United States Patent and Trademark Office requesting exclusive rights to use the slogan for political purposes. It was registered as a service mark on July 14, 2015, after Trump formally began his 2016 presidential campaign and demonstrated that he was using the slogan for the purpose stated on the application.

"Vote To Make America Great Again" banner in California, 2016

 

MAGA placard

However, Trump did not trademark the phrase in commerce. On August 5, 2015, radio personality Bobby Bones took note of this and successfully filed a trademark for the phrase's use in commerce. Two days later Bones tweeted at Trump, offering the use of his slogan back in exchange for a $100,000 donation to the St. Jude Children's Research Hospital. On October 29, Bones followed up the tweet with an image of a check from the Trump Organization. The amount on the check was undisclosed and Bones said that Trump could "have [his] slogan back".

Following Trump's first election, the website of his presidential transition was established at greatagain.gov. Trump said in 2017 and 2018 that the slogan of his 2020 reelection campaign would be "Keep America Great" and he sought to trademark it. However, Trump's 2020 campaign continued to use the "Make America Great Again" slogan. Trump's vice president, Mike Pence, used the phrase "make America great again, again" in his 2020 Republican National Convention speech, garnering ridicule for implying that Trump's first term had failed. In late 2021, this phrase became the name of a pro-Trump Super-PAC, which was also mocked. A 2020 executive order, titled "Promoting Beautiful Federal Civic Architecture", was nicknamed "Make Federal Buildings Beautiful Again" by proponents and the press.

Less than a week after Trump left office, he spoke to advisors about possibly establishing a third party, which he suggested might be named either the "Patriot Party" or "Make America Great Again Party". In his first few days out of office, he also supported Arizona state party chairwoman Kelli Ward, who likewise called for the creation of a "MAGA Party". In late January 2021, the former president viewed the proposed MAGA Party as leverage to prevent Republican senators from voting to convict him during the Senate impeachment trial, and to field challengers to Republicans who voted for his impeachment in the House. The phrase was used again as the official slogan of Trump's 2024 presidential campaign. On June 3, 2023, Trump called his supporters Magadonians, prompting mockery on social media.

MAGA hat

"Maga hat" redirects here; not to be confused with Magahat.

Trump with a "Keep America Great" hat in December 2019, and Elon Musk wearing a black MAGA hat at the 2025 Conservative Political Action Conference

During the 2016 campaign, Trump often used the slogan, especially by wearing MAGA hats emblazoned with the phrase in white letters, which soon became popular among his supporters. The slogan was so important to the campaign that at one point it spent more on making the hats – sold for $25 each on its website – than on polling, consultants, or television commercials. Millions were sold, and Trump estimated that counterfeit versions outnumbered the real hat ten to one. "... but it was a slogan, and every time somebody buys one, that's an advertisement." The hat's white-on-red design saw great success as a symbol of unity among Trump supporters.

Some critics have compared its use to other politically charged symbols, such as the Confederate flag, while supporters view it as an expression of patriotism and political identity. Due to its association with Trump and his policies, the hat has been a source of controversy. Some individuals view it as a divisive or provocative symbol, while others see it as an exercise of their political beliefs.

Trump supporters wearing MAGA hats, October 2025

In January 2019, it gained media attention during a highly publicized standoff between a group of high schoolers wearing the hat and Omaha tribe leader Nathan Phillips. The incident was initially perceived by some as racially charged; however, subsequent video footage led to a reassessment of the situation by multiple media outlets. On December 29, 2022, the 9th Circuit Court of Appeals in Vancouver, Washington, ruled that wearing a MAGA hat is considered protected speech under the First Amendment.  A former teacher had worn a MAGA hat to class to school and described facing verbal harassment and retaliation from school employees.

Use on social media sites

See also: Social media use by Donald Trump

Donald Trump took the campaign slogan to social media (primarily to Twitter), using the hashtags #makeamericagreatagain and its acronym #maga. In response to criticism regarding his frequent and untraditional usage of social media, Trump defended himself by tweeting "My use of social media is not Presidential – it's MODERN DAY PRESIDENTIAL. Make America Great Again!" on July 1, 2017.

In the first half of 2017, Trump posted his slogan on Twitter 33 times. In an article for Bloomberg News, Mark Whitehouse noted: "A regression analysis suggests the phrase adds (very roughly) 51,000 to a post's retweet-and-favorite count, which is important given that the average Trump tweet attracts a total of 107,000." Trump attributed his victory (in part) to social media when he said, "I won the 2016 election with interviews, speeches, and social media." According to RiteTag, the estimated hourly statistics for #maga on Twitter alone include: 1,304 unique tweets, 5,820,000 hashtag exposure, and 3,424 retweets with 14% of #maga tweets including images, 55% including links, and 51% including mentions.

Accusations of racism

See also: Racial views of Donald Trump

Further information: Racism in the United States and Historical racial and ethnic demographics of the United States

Regarding its use since 2015, the phrase "Make America Great Again" is considered a loaded phrase and "dog whistle" by some. Marissa Melton, a Voice of America journalist, among others, argue it is a loaded phrase because it "doesn't just appeal to people who hear it as racist coded language, but also to those who have felt a loss of status as other groups have become more empowered." Sarah Churchwell argues the slogan now resonates as "America First" did in the early 1940s, with the idea "that the true version of America is the America that looks like me, the American fantasy I imagine existed before it was diluted with other races and other people."

Writing opinion for the Los Angeles Times, Robin Abcarian wrote that "[w]earing a 'Make America Great Again' hat is not necessarily an overt expression of racism. But if you wear one, it's a pretty good indication that you share, admire or appreciate President Trump's racist views about Mexicans, Muslims and border walls." The Detroit Free Press and the Los Angeles Times reported how several of their readers rejected this characterization and did not believe the slogan or MAGA hats are evidence of racism, seeing them more in patriotic or American nationalist terms. Los Angeles Times columnist Nicholas Goldberg described MAGA as both one of the worst campaign slogans ever and "a fabulous campaign slogan", writing: "It was vague enough to appeal to optimists generally, while leaving plenty of room for bitter and resentful voters to conclude that we were finally going back to the days when they ran the world." Actor Bryan Cranston said of the slogan: "So just ask yourself from, from an African American experience, when was it ever great in America for the African American? When was it great? If you're making it great again, it's not including them."

A 2018 study that used text mining and semantic network analytics of Twitter text and hashtags networks found that the "#MakeAmericaGreatAgain" and "#MAGA" hashtags were commonly used by white supremacist and white nationalist users, and had been used as "an organizing discursive space" for far-right extremists globally.

Derivative slogans

"Make America Great Again" has been the subject of many parodies, jokes, instances of praise, references, and criticisms which base themselves on the four-word slogan.

Derivatives used by Trump

"Keep America Great" has been the most popular derivative of "Make America Great Again", with Trump's 2020 presidential campaign adopting it as the official slogan, though often used alongside "Make America Great Again". Upon Trump announcing his candidacy for president in the 2024 election, commentators described his use of the tagline "Make America Great and Glorious Again" ("MAGAGA"). The term has come to be a humorous descriptor for Trump's re-election bid, and many outlets have commented on the humor that "MAGAGA" provides, usually on the word "gag" being part of the acronym.

At the 2024 Republican National Convention, some people wore clothing with the slogan "Make American Great Again Again". In October 2024, Trump promised former third-party candidate Robert F. Kennedy Jr. control of public health using the phrase "Make America Healthy Again". In November 2024, after Governor Gavin Newsom pledged to convene California lawmakers to secure California's progressive policies against the incoming Trump administration, Trump made "Make California Great Again" go viral on social media.

During a joint press conference with Philippine president Bongbong Marcos at the White House in July 2025, Trump voiced support for the Philippines' independent foreign policy and said, "I think he (Marcos) has to do what's right for his country. I've always said, you know, make the Philippines great again. Do whatever you need to do."

In January 2026, Trump said of Venezuelan vice president Delcy Rodríguez, "She's essentially willing to do what we think is necessary to make Venezuela great again." Rodríguez became acting president after the Trump administration captured Venezuelan president Nicolás Maduro and his wife and took them into custody in New York.

Make Iran Great Again

The slogan "Make Iran Great Again," coined by U.S. president Donald Trump, characterizes the Islamic regime as damaging Iran, and advocates for its replacement to help Iran become a stronger nation. This slogan was also used by the Iranian opposition group Restart.

Anti-Trump derivatives, parodies, and other derivatives

Two women wear "Make Donald Drumpf Again" hats during the 2017 Women's March.

The phrase has been parodied in political statements, such as "Make America Mexico Again", a critique of Trump's immigration policies regarding the US–Mexico border and a reference to Mexico's loss of 55% of its territory to the US with the Treaty of Guadalupe Hidalgo.

Adult film star Stormy Daniels, who allegedly had an affair with President Trump, took part in a "Make America Horny Again" strip club tour. The tour followed Trump's initial 2016 campaign trail and part of the revenue was donated to Planned Parenthood. John Oliver spoofed the slogan on his show Last Week Tonight with John Oliver in a segment dedicated to Trump, urging viewers to "Make Donald Drumpf Again", in reference to the original ancestral name of the Trump family. The segment broke HBO viewership records, garnering 85 million views.

In 2017, after the certification of the election of Trump by Congress, then-Vice-president Joe Biden was heard saying "God Save the Queen", leading to History Today claiming it would get "Make America Great Britain Again". Later in the year, comedian Jimmy Kimmel repeated the phrase to suggest limiting presidential power. A 2018 essay about the Barack Obama birtherism conspiracy in the Journal of Hate Studies by two professors at Bates College was titled "Make America Hate Again: Donald Trump and the Birther Conspiracy".

A 2019 "Make Earth Greta Again" protestor in Berlin

The phrase has been adopted by some environmentalists. In June 2017, French president Emmanuel Macron rebuked Trump over withdrawing from the Paris Agreement. The last sentence of the speech he delivered was "make our planet great again". Members of the Fridays for Future Movement have also frequently used slogans like "Make Earth Greta Again", referring to environmental activist Greta Thunberg. In 2019, Grant Armour and Milene Larsson co-directed a documentary film named Make the World Greta Again. After Joe Biden defeated Trump in the 2020 presidential election, Biden's wife Jill posted an image of her and her husband on Instagram which featured Joe wearing a blue cap with white text reading "We Just Did", meant as a response to Trump's "Make America Great Again" slogan.

In late 2022, the political slogan "MAGA Communism" trended on Twitter after being tweeted out by former San Clemente city council candidate Jackson Hinkle. MAGA Communism adherents call on those who support the American working class to ally with members of the MAGA movement. During the presidential campaign Javier Milei in Argentina in 2023, the slogan MAGA was adapted as "Make Argentina Great Again". Milei, a personal friend, as well as an admirer of Trump, later won the election in November 2023, with Trump sending a congratulatory message with the slogan "Make Argentina Great Again". The term "Blue MAGA" is used to criticize a cult-like dedication to Biden as a person, the Democratic Party's use of conspiracy theories to explain opposition to Biden's 2024 presidential candidacy, and dismissals of information or polling that does not reflect well on Biden; the term seeks to suggest an equivalence between some supporters of Biden and Trump.

In early 2025, the Brazilian government's secretary of communication Sidônio Palmeira created the slogan "Brazil belongs to Brazilians" ("O Brasil é dos brasileiros")—printed on blue caps—at the request of then-on-leave secretary of institutional affairs Alexandre Padilha, with the aim of countering the "Make America Great Again" caps. Brazil's president Luiz Inácio Lula da Silva also posted a video on social media wearing the cap, in what has sometimes been referred to as the "battle of the caps" or "cap war". Padilha stated he was distressed to see people "saluting another country", in reference to former president Jair Bolsonaro; also following Trump's inauguration, the governor of São Paulo Tarcísio de Freitas appeared wearing a red cap with the phrase "Make America Great Again". In July of that year, after Trump's tariff hike against Brazil which was described by The Economist as the greatest interference since the Cold War, Lula da Silva adopted a nationalist stance, once again wearing a cap bearing the slogan "Brazil belongs to Brazilians". In August, during the second ministerial meeting of the year, Lula da Silva and his ministers wore the cap in question; the president and all 38 ministers posed for identification-style photographs on the occasion.

A Make America Go Away hat design, featuring Greenland's flag on the side

The slogan "Make America Go Away" was seen in the 2026 Hands off Greenland protests.

Use of the slogan by Trump's political rivals

After Donald Trump popularized the use of the phrase, the phrase and modifications of it were widely used in reference both to his election campaign and to his politics. Trump's primary opponents, Ted Cruz and Scott Walker, began using "Make America Great Again" in speeches, inciting Trump to send cease-and-desist letters to them. Cruz later sold hats featuring "Make Trump Debate Again" in response to Trump's boycotting the Iowa January 28, 2016, debate.

New York governor Andrew Cuomo said America "was never that great" during a September 2018 bill signing. Former United States attorney general Eric Holder questioned the slogan in a March 2019 interview on MSNBC, asking: "Exactly when did you think America was great?" During John McCain's memorial service on September 1, 2018, his daughter Meghan stated: "The America of John McCain has no need to be made great again because America was always great." Trump subsequently tweeted "MAKE AMERICA GREAT AGAIN!" later that day. Throughout Donald Trump's presidencies, the phrase "Is America great yet?" has been used both facetiously and as an actual poll question.

During remarks at the White House on May 4, 2022, President Joe Biden referred to former president Trump's Make America Great Again movement, saying, "This MAGA crowd is really the most extreme political organization that's existed in American history, in recent American history." On September 1, 2022, he dedicated remarks at the White House "on the continued battle for the soul of the nation", to attacks on "Donald Trump and the MAGA Republicans", saying that "Donald Trump and the MAGA Republicans represent an extremism that threatens the very foundations of our republic", and that "MAGA Republicans have made their choice. They embrace anger. They thrive on chaos. They live not in the light of truth but in the shadow of lies."

After Florida governor Ron DeSantis announced his run for the 2024 Republican Party presidential primary, several news outlets said he promised to "Make America Florida". One of the most widespread anti-Trump derivatives of "Make America Great Again" during the Trump presidency and the 2020 election was "Make America Think Again", often combined with 2020 Democratic primary candidate Andrew Yang's preferred version of "Make America Think Harder" ("MATH"). The slogan has been spotted at numerous anti-Trump events from Democratic political rallies to marches to social media, with Live Science noting "Think Again" as one of its top hashtags for 2017.

"Make America White Again"

Australian political commentator and former Liberal Party leader John Hewson used the satirical slogan "Make America White Again" in reference to his belief that recent global movements against traditional politics and politicians are based on racism and prejudice. He comments: "There should be little doubt about U.S. president Donald Trump's views on race, despite his occasional 'denials', assertions of 'fake news', and/or his semantic distinctions. His election campaign theme was effectively a promise to 'Make America Great Again; America First and Only' and—nod, nod, wink, wink—to Make America White Again." In a bid for attention, a few fringe racist political activists have associated "Make America Great Again" with a return to whiteness.

In popular culture

See also: Works with titles derived from Make America Great Again

"Make America Great Again" has frequently been parodied in advertising, the media, and other outlets of popular culture, with varying levels of comparison to Trump from none at all to a rebuke of the former president and his ideology.

In advertising

The slogan was parodied by Dunk-a-roos as "Make America Dunk Again", and also in the film Sharknado 5: Global Swarming's tagline of "Make America Bait Again."

In artwork

Make Everything Great Again was a street art mural by artist Mindaugas Bonanu in Vilnius, Lithuania. Inspired by the graffiti painting My God, Help Me to Survive This Deadly Love, it depicts Donald Trump giving a fraternal kiss to the Russian president Vladimir Putin.

In fashion

Fashion designer Andre Soriano used the "Make America Great Again" official presidential campaign flag to design a MAGA gown for celebrities in Hollywood to wear on red carpet, such as at the 2017 Grammy Awards.

In films and web series

The tagline for the film The Purge: Election Year (2016) is "Keep America Great" (a phrase Trump would later use as his 2020 campaign slogan); one of the TV spots for the film featured Americans who explain why they support the Purge, with one stating he does so "to keep my country [America] great". The next film in the franchise, The First Purge, was subsequently advertised with a poster featuring its title stylized on a MAGA hat. In The Boys Season 4, the political slogan "Make America Super Again" serves as the main rallying cry for Homelander, the primary antagonist, as he successfully executes his own version of January 6 coup attempt in the universe of The Boys franchise.

In literature

Author Octavia E. Butler used "Make America Great Again" as the presidential campaign slogan for the dictator Andrew Steele Jarret in her 1998 dystopian novel Parable of the Talents. In 2011, Republican former United States Senate candidate Christine O'Donnell published a book about her campaign in the 2010 Delaware special election titled Troublemaker: Let's Do What It Takes to Make America Great Again. Political advisor Dan Pfeiffer's second book is called Un-Trumping America: A Plan to Make America a Democracy Again. Political commentator and author Peter Beinart published a 2006 book titled The Good Fight: Why Liberals – and Only Liberals – Can Win the War on Terror and Make America Great Again, drawing on the philosophy of theologian Reinhold Niebuhr after the 2003 invasion of Iraq and during the early years of the war on terror.

In music

Snoop Dogg's second EP is called Make America Crip Again with the second single titled "M.A.C.A." Dogg was quoted in Rolling Stone as saying that "Make America Great Again" refers to a time in the past that "always takes me back to separation and segregation so I'd rather Make America Crip Again" and referred to a time "when young black men in impoverished areas organized to help their communities and to take care of their own because society basically left them for dead". Singer Joy Villa produced a single "Make America Great Again" a few months after appearing at the 2017 Grammy Awards in a 'MAGA' dress. Australian heavy metal band Thy Art Is Murder recorded a song called "Make America Hate Again" on their album Human Target.

On television

The Star Trek: Discovery episode "What's Past Is Prologue" has Gabriel Lorca vowing in one scene to "Make the Empire glorious again". In the South Park episode "Where My Country Gone?" (2015), supporters of Mr. Garrison, who runs a campaign that is a parody of Trump's, are seen holding signs bearing the slogan.

In video games

Senator Armstrong, the antagonist of the 2013 video game Metal Gear Rising: Revengeance uses the phrase "make America great again". Wolfenstein II: The New Colossus, a first-person shooter video game with Nazis as the enemy, was given the advertising tagline "Make America Nazi-Free Again", which some people objected to as anti-Trump, though a company executive said the game was not a "social critique on 2017 America." Peters Hines, the studio's vice president of marketing and public relations, was quoted on GamesIndustry.biz as saying, "Wolfenstein has been a decidedly anti-Nazi series since the first release more than 20 years ago. We aren't going to shy away from what the game is about. We don't feel it's a reach for us to say Nazis are bad and un-American, and we're not worried about being on the right side of history here."

Similar slogans used outside the United States

During his campaign for the 2019 Indonesian presidential election in October 2018, former opposition leader Prabowo Subianto used the phrase "make Indonesia great again", though he denied having copied Trump. During the Swedish European Parliament election in May 2019, the Christian Democrats party used the slogan "Make EU Lagom Again". The Spanish far-right party Vox used "Hacer a España grande otra vez" (Make Spain Great Again) as a slogan.

During the 2020 Trinidad and Tobago general election campaign, the Leader of the Opposition and former prime minister Kamla Persad-Bissessar, who has been accused as attempting to be a "wannabe Trinidad and Tobago Trump," used the phrase "Make T&T (Trinidad and Tobago) great again!" Following Donald Trump's victory in the 2024 United States presidential election, she described his win as an effort to "restore conservative American values and ideals, which have been under attack by promoters of extreme far-left ideology." In Singapore, the slogan "Make Yishun Great Again" was used by content creators as a running joke where the town itself has a stereotype for being dangerous. There were hats sold with the phrase. Similarly, People's Power Party, a political party in Singapore, used a variant of the slogan, "Make Singapore Home Again" for their party's manifesto and campaign during the 2025 Singaporean general election.

The right-wing populist United Australia Party used the slogans "Make Australia Great" and "Make Australia Great Again" during the 2019 and 2022 Australian federal elections. Coalition senator Jacinta Nampijinpa Price used the phrase "make Australia great again" during the 2025 federal election campaign. At a later press conference, she said she hadn't "even realise[d]" she said the phrase and accused media outlets of being "obsessed with Donald Trump". In Israel, the Israeli far-right has used the similar expression "Make Israel Great Again" along with the acronym MIGA. In Mongolia, Khaltmaagiin Battulga used as his 2017 presidential election campaign slogan "Монгол Ялна" (Mongol Yalna, "Mongolia Will Win"), with its abbreviation "Мояа" (Moya) being a derivative term. The 2024 Hungarian Presidency of the Council of the European Union used the motto "Make Europe Great Again" (MEGA). In the Philippines, Isko Moreno used the slogan "Make Manila Great Again" for his mayoral campaign during the 2025 Manila local elections.

In January 2025, during an Alternative für Deutschland (AfD) campaign rally for the 2025 German federal election, Elon Musk spoke at the event through a video call, reiterating his previous endorsement of the party. Following his short speech, Alice Weidel, the leading AfD candidate for the upcoming elections, thanked Musk and used the derived expression "Make Germany great again". In February 2025, Indian prime minister Narendra Modi used the derivative "Make India Great Again" during a bilateral meeting with Trump, saying: "Borrowing an expression from the US, our vision for a developed India is to 'Make India Great Again', or MIGA. When America and India work together, when it's MAGA plus MIGA, it becomes mega – a mega partnership for prosperity." An April 2025 article by The Economist which introduced the impact of the second Trump administration tariffs in China was entitled "How America could end up making China great again". In Syria, a billboard was seen in Damascus during the visit of U.S. Republican congressman Cory Mills, displaying the phrase "Make Syria Great Again." In an interview with the Jewish Journal on May 28, 2025, Syrian president Ahmed al-Sharaa said he accepted the role to help rebuild Syria, stating, "We have no choice but to succeed", and used the phrase "We must make Syria great again".