A statistical method used to determine if there is enough evidence to reject a null hypothesis, based on sample data.

Analysis of Variance; a technique used to compare means among three or more groups to assess if at least one group mean is different.

Multivariate Analysis of Variance; an extension of ANOVA that assesses multiple dependent variables simultaneously.

Statistical techniques used to analyze data that involves multiple variables, helping to understand relationships and effects.

A measure that helps determine the significance of results in hypothesis testing; a lower p-value indicates stronger evidence against the null hypothesis.

The incorrect rejection of a true null hypothesis; also known as a false positive.

The failure to reject a false null hypothesis; also known as a false negative.

A statistical method for modeling the relationship between a dependent variable and one or more independent variables.

Also known as R-squared; it indicates the proportion of variance in the dependent variable explained by the independent variables.

Techniques used to assess the validity and fit of a statistical model, ensuring accurate interpretations.

The process of making sense of numerical data, identifying trends, and drawing conclusions based on statistical analyses.

The graphical representation of data to help communicate findings clearly and effectively to diverse audiences.

A concise overview of a report's key findings and recommendations, aimed at decision-makers.

A determination that the observed results are unlikely to have occurred by chance, typically assessed using p-values.

A regression analysis technique that models relationships between one dependent variable and multiple independent variables.

A collection of data points organized for analysis, which can include various types of variables.

A variable that can take on one of a limited, fixed number of possible values, representing different categories.

A variable that can take an infinite number of values within a given range, often measured on a scale.

The number of observations or data points collected for analysis; larger sizes generally yield more reliable results.

A range of values that is likely to contain the true population parameter, reflecting the uncertainty of an estimate.

A data point that differs significantly from other observations, potentially indicating variability or error.

The process of identifying and correcting inaccuracies or inconsistencies in data to ensure quality analysis.

Tools and applications used for conducting statistical analyses, such as R, SPSS, or Python libraries.

The practice of conveying statistical findings in a clear, engaging manner, tailored to the audience's level of understanding.

A process in which outputs of a system are circled back and used as inputs, enhancing learning and improvement.