Meta-analysis. The use of statistical techniques to combine the findings of multiple independent studies. Example
Systematic review. A systematic method of identifying, selecting, appraising, and synthesizing the findings of all investigations of a specific research question. Example
Randomized controlled trial. An interventional study in which people are randomly assigned to two or more groups that receive different treatment. Example
Cohort study. A prospective observational study in which a group of people who have a certain condition or receive a particular treatment are followed over time and compared with another group of people who do not have that condition or receive that treatment. Example
Case-control study. A retrospective observational study in which two groups of people who have experienced different outcomes are compared on the basis of a supposed causal attribute. Example
Cross-sectional study. An observational study in which the prevalence of a condition within a given population of people is assessed at one time point. Example
Case report or case series. A detailed description of a rare finding in one or more patients. Example
t-test. Used to test whether two groups of data are significantly different from each other. A paired t-test is applied to dependent samples (e.g., pre-treatment vs. post-treatment), whereas an unpaired t-test is applied to independent samples (e.g., treatment group vs. control group).
Analysis of variance (ANOVA). Generally used to test whether more than two groups of data are significantly different from each other.
Correlation. Tests the association between two variables. A positive correlation means that both variables increase or decrease together. A negative correlation means that as one variable increases, the other variable decreases.
Regression. Tests the relationship between predictor variables and an outcome variable. In linear regression the outcome variable is continuous, whereas in logistic regression the outcome variable is binary
Survival analysis. Analyzes the time duration until an event happens (e.g., death).
Sensitivity and specificity. Indicates the probability that a test will yield a correct response.
Effect size. Measures the strength of a treatment effect.
Power analysis. Used to estimate the sample size required for detecting significant differences between groups.