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Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. They are typically used at the beginning of data analysis to provide an overview of the dataset.

Inferential statistics allow you to make predictions ('inferences') about a population based on a sample of data taken from that population. They are typically used to determine if there is a significant effect or association present in the data.

This technique measures the extent to which two variables are related. It is typically used to determine if an association exists between variables and how strong that association might be.

Regression analysis is a predictive modeling technique that analyzes the relationship between a dependent (target) and independent variable(s) (predictor). It is often used to predict outcomes and to understand which among the variables are related to the outcome.

Factor analysis is a type of analysis that is used to identify underlying variables, or 'factors', that explain the pattern of correlations within a set of observed variables. It is typically used in test construction, to reduce data dimensions, or to identify latent constructs.

PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. It's typically used for dimensionality reduction in data pre-processing.

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. It is typically used in market research, pattern recognition, and data analysis for categorization.

ANOVA is a technique that is used to compare the means of three or more samples (using the F distribution). This test is typically used when testing for differences between groups in experimental designs.

Like ANOVA, but MANOVA allows for the comparison of multiple dependent variables across different groups simultaneously. It is typically used when the study requires the examination of the effects of independent variables on multiple dependent variables.

A confidence interval gives an estimated range of values which is likely to include an unknown population parameter. It is typically used to indicate the reliability of an estimate.

The Chi-Square test is used to determine whether there is a significant association between two categorical variables. It's typically used with count data and contingency tables.

These tests don't rely on data belonging to any particular parametric family of probability distributions. They are useful when data do not meet the assumptions required for parametric tests, for example with ordinal data or non-normal distributions.

SEM is a multivariate statistical analysis technique that is used to analyze structural relationships. It combines factor analysis and multiple regression analysis, and it is often used in social sciences research to model complex relationships.

Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. It's typically used for forecasting and understanding seasonal patterns in data.

Survival analysis is used to analyze data in which the time until the event is of interest. It is typically used in medical research for time-to-event data, where the event might be death, occurrence of disease, etc.

Meta-analysis combines the results of several studies that address a set of related research hypotheses. It is typically used in evidence-based practices to summarize the effects of interventions.

Path analysis is a straightforward extension of multiple regression. Its aim is to provide estimates of the magnitude and significance of hypothesized causal connections between sets of variables. It is typically used in SEM frameworks.

LGM is used to estimate growth trajectories over time. It is an extension of SEM that allows estimation of latent growth processes. It's typically used for analyzing longitudinal data.

IRT is used to analyze the properties of tests and questionnaires. It is based on the idea that the probability of a correct answer to an item is a mathematical function of person and item parameters. It's often used in educational testing and psychological scale development.

Multilevel modeling is used for data that has a hierarchical structure, analyzing how a response variable is influenced by variables at different levels. It's typically used in data where individuals are nested within larger units like schools or clinics.

Discriminant analysis is used when you have one or more normally distributed interval independent variables and a categorical dependent variable. It is a classification technique to predict group membership.

Logistic regression is used when the dependent variable is categorical to estimate the probability of a binary outcome based on one or more predictor variables. It is used extensively in fields like medicine and social sciences.

This technique explores relationships between two multivariate sets of variables. It's used when there are two sets of variables and the goal is to understand the relationships between the two.

These are statistical measures of the performance of a binary classification test. Sensitivity (True Positive rate) and Specificity (True Negative rate) are typically used in the fields of medical testing and biostatistics.