Design of experiments

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Design of experiments, or experimental design, (DoE) is the design of all information-gathering exercises where variation is present, whether under the full control of the experimenter or not. (The latter situation is usually called an observational study.) Often the experimenter is interested in the effect of some process or intervention (the "treatment") on some objects (the "experimental units"), which may be people, parts of people, groups of people, plants, animals, etc. Design of experiments is thus a discipline that has very broad application across all the natural and social sciences.

Additional info

Absolute risk reduction

In epidemiology, the absolute risk reduction is the decrease in risk of a given activity or treatment in relation to a control activity or treatment. It is the inverse of the number needed to treat.^[1]

Academic clinical trials

Academic clinical trials are a valuable component of the health care system; they benefit patients and help determine the safety and efficacy of new drugs and devices.

Analysis of clinical trials

Failure to include all participants in the analysis may bias the trial results. Most trials do not yield perfect data, however. "Protocol violations" may occur, such as when the patients do not receive the full intervention or the correct intervention or a few ineligible patients are randomly allocated in error. Despite the fact that the most clinical trials are carefully planned, many problems can occur during the conduct of the study. Some examples are as follows:

Analysis of covariance

Analysis of covariance (ANCOVA) is a general linear model with one continuous outcome variable (quantitative) and one or more factor variables (qualitative). ANCOVA is a merger of ANOVA and regression for continuous variables. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative predictors (covariates) account. The inclusion of covariates can increase statistical power because it accounts for some of the variability.

Analysis of variance

In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance is partitioned into components due to different explanatory variables. In its simplest form ANOVA gives a statistical test of whether the means of several groups are all equal, and therefore generalizes Student's two-sample t-test to more than two groups. ANOVAs are helpful because they possess a certain advantage over a two-sample t-test. Doing multiple two-sample t-tests would result in a largely increased chance of committing a type I error. For this reason, ANOVAs are useful in comparing three or more means.

Animal testing

Main articles
Animal testing
Alternatives to animal testing
Testing on: invertebrates
frogs · primates
rabbits · rodents
Animal testing regulations
History of animal testing
History of model organisms
IACUC
Laboratory animal sources
Pain and suffering in lab animals
Testing cosmetics on animals
Toxicology testing
Vivisection

Animal testing on non-human primates

Experiments involving non-human primates (NHPs) include toxicity testing for medical and non-medical substances; studies of infectious disease, such as HIV and hepatitis; neurological studies; behavior and cognition; reproduction; genetics; and xenotransplantation. Around 65,000-70,000 are used every year in the United States and European Union. Most are purpose-bred, while some are caught in the wild.^[1]

Arithmetic mean

In mathematics and statistics, the arithmetic mean (or simply the mean) of a list of numbers is the sum of all of the list divided by the number of items in the list. If the list is a statistical population, then the mean of that population is called a population mean. If the list is a statistical sample, we call the resulting statistic a sample mean.

Association (statistics)

In statistics, an association is any relationship between two measured quantities that renders them statistically dependent.^[1] The term "association" refers broadly to any such relationship, whereas the narrower term "correlation" refers to a linear relationship between two quantities.

Bar chart

A bar chart or bar graph is a chart with rectangular bars with lengths proportional to the values that they represent. Bar charts are used for comparing two or more values that were taken over time or on different conditions, usually on small data sets. The bars can be horizontal lines or it can also be used to mass a

Bayes estimator

In estimation theory and decision theory, a Bayes estimator or a Bayes rule is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior of a utility function.

Bayesian experimental design

Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment. This allows accounting for both any prior knowledge on the parameters to be determined as well as uncertainties in observations.

Bayesian inference

Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name "Bayesian" comes from the frequent use of Bayes' theorem in the inference process. Bayes' theorem was derived from the work of the Reverend Thomas Bayes.^[1]

Bayesian probability

Bayesian probability is one of the most popular interpretations of the concept of probability. The Bayesian interpretation of probability can be seen as an extension of logic that enables reasoning with uncertain statements. To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new relevant data. The Bayesian interpretation provides a standard set of procedures and formula to perform this calculation.

Bayesian statistics

Best linear unbiased estimator

In statistics, the Gauss–Markov theorem, named after Carl Friedrich Gauss and Andrey Markov, states that in a linear model in which the errors have expectation zero and are uncorrelated and have equal variances, a best linear unbiased estimator (BLUE) of the coefficients is given by the ordinary least squares estimator. The errors are not assumed to be normally distributed, nor are they assumed to be independent (but only uncorrelated — a weaker condition), nor are they assumed to be identically distributed (but only having zero mean and equal variances).

Best linear unbiased prediction

In statistics, best linear unbiased prediction (BLUP) is used in linear mixed models for the prediction of random effects. BLUP was derived by Charles Roy Henderson (Henderson, 1975). Best linear unbiased predictions (BLUPs) of random effects are equivalent to best linear unbiased estimates (BLUEs) (see Gauss–Markov theorem) of fixed effects. The distinction arises because it is conventional to talk about estimating fixed effects but predicting random effects, but the two terms are otherwise equivalent. BLUP is used in animal breeding to estimate genetic merits.

Binomial regression

In statistics, binomial regression is a technique in which the response (often referred to as Y) is the result of a series of Bernoulli trials, or a series of one of two possible disjoint outcomes (traditionally denoted "success" or 1, and "failure" or 0).^[1] In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables.

Biomedical research

Biomedical research (or experimental medicine), in general simply known as medical research, is the basic research, applied research, or translational research conducted to aid and support the body of knowledge in the field of medicine. Medical research can be divided into two general categories: the evaluation of new treatments for both safety and efficacy in what are termed clinical trials, and all other research that contributes to the development of new treatments. The latter is termed preclinical research if its goal is specifically to elaborate knowledge for the development of new therapeutic strategies. A new paradigm to biomedical research is being termed translational research, which focuses on iterative feedback loops between the basic and clinical research domains to accelerate knowledge translation from the bedside to the bench, and back again.

Biplot

Biplots are a type of graph used in statistics. A biplot allows information on both samples and variables of a data matrix to be displayed graphically. Samples are displayed as points while variables are displayed either as vectors, linear axes or nonlinear trajectories. In the case of categorical variables, category level points may be used to represent the levels of a categorical variable. A generalised biplot displays information on both continuous and categorical variables.

Blind experiment

The blind method is a part of the scientific method, used to prevent research outcomes from being influenced by either the placebo effect or the observer bias. To blind a person involved in research (whether a researcher, subject, funder, or other person) is to prevent them from knowing certain information about the process. The terms 'blind' (adj) or 'to blind' (vt) when used in this sense are figurative extensions of the literal idea of blindfolding someone. Blinded research is an important tool in many fields of research, from medicine, to psychology and the social sciences, to forensics.

Blinding (medicine)

Blocking (statistics)

In the statistical theory of the design of experiments, blocking is the arranging of experimental units in groups (blocks) that are similar to one another. For example, an experiment is designed to test a new drug on patients. There are two levels of the treatment, drug, and placebo, administered to male and female patients in a double blind trial. The sex of the patient is a blocking factor accounting for treatment variability between males and females. This reduces sources of variability and thus leads to greater precision.