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ANOVA
A statistical method used for comparing the means of three or more groups.
F Ratio
The ratio of variance between groups to the variance within groups; larger F ratios indicate a more likely significant ANOVA.
Power
The probability of correctly rejecting a false null hypothesis.
Effect Size
A measure of how much the group means differ from one another; can indicate small or large differences.
One Way ANOVA
A design that involves one independent variable with three or more levels to compare group means.
Two Way ANOVA
An analysis involving two independent variables, assessing both their main effects and interactions.
Repeated Measures ANOVA
A method where each subject is tested under all experimental conditions, controlling for individual differences.
Mixed Design ANOVA
An ANOVA that includes at least one independent factor and one repeated factor.
Post Hoc Multiple Comparisons
Exploratory tests carried out after a significant ANOVA to determine which means are different.
Chi Square
A statistical test used to assess the association between categorical variables and test the significance of proportions.
Goodness of Fit
A test to determine if observed data fits a specified distribution or expected proportions.
Standardized Residuals
Measures used to show which categories contribute most to the chi square value, highlighting variances from expected values.
Correlation
A measure of association between two variables, indicating the strength and direction of their relationship.
Correlation Coefficient
A numerical measure that assesses the strength and direction of the relationship between two variables, ranging from -1 to +1.
Linear Regression
A method to predict the value of a dependent variable based on an independent variable, while assessing shared variance.
Multiple Regression
A regression analysis involving multiple independent variables predicting one dependent variable.
Odds Ratio
A measure indicating how much more likely an individual is to belong to a target group compared to a reference group.
Null Hypothesis
The hypothesis that there is no effect or no difference, commonly denoted as H0.
Alternative Hypothesis
The hypothesis that there is a significant effect or difference, typically denoted as H1.
Type I Error
The error made when the null hypothesis is incorrectly rejected, also known as a false positive.
Type II Error
The error made when the null hypothesis is incorrectly accepted, also known as a false negative.
Statistical Significance
A determination that an observed effect in data is likely not due to random chance, typically assessed via p-values.
p-value
The probability of observing data as extreme as the sample, assuming the null hypothesis is true.
Factorial ANOVA
An analysis of variance that evaluates two or more independent variables at the same time.
Interaction Effect
A situation in ANOVA where the effect of one independent variable on the dependent variable differs depending on the level of another independent variable.
Assumption of Homogeneity
The assumption that different samples have similar variances; important for the validity of ANOVA results.
Confidence Interval
A range of values derived from sample statistics that is likely to contain the population parameter.
Statistical Power
The likelihood that a study will detect an effect when there is an effect to be detected.
Sample Size
The number of observations or replicates included in a statistical sample.
Parametric Test
Statistical tests that assume data comes from a type of probability distribution and generally require interval data.
Non-parametric Test
Statistical tests not based on parameterized family of probability distributions; useful for ordinal data.
Corrected p-value
p-value adjusted to account for multiple testing errors, often using methods like Bonferroni correction.
Control Group
The group in an experimental study that does not receive the treatment being tested, used for comparison.
Experimental Group
The group in a study that receives the treatment or intervention being tested.
Random Assignment
The process of randomly assigning participants to different experimental groups to reduce bias.
Moderator Variable
A variable that affects the strength or direction of the relation between an independent and dependent variable.
Mediator Variable
A variable that explains the relationship between the independent and dependent variables.
Longitudinal Study
Research that follows the same subjects over a period of time to observe changes and developments.
Cross-sectional Study
A study that analyzes data from a population at a specific point in time.
Descriptive Statistics
A summary of the basic features of a dataset, providing simple summaries about the sample and measures.
Inferential Statistics
Statistical methods that allow us to use sample data to make generalizations about a population.
Outliers
Data points that differ significantly from the rest of the data, which can affect statistical analyses.
Normal Distribution
A probability distribution that is symmetric about the mean, showing that data near the mean are more frequent.
Skewness
A measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
Kurtosis
A descriptor of the shape of the probability distribution of a real-valued random variable, indicating tails and peak.
Confidence Level
The percentage of times you can expect to get the same result if you were to repeat the same test numerous times.
Sampling Bias
A bias in selecting a sample that is not representative of the population being studied.
Response Bias
A tendency for respondents to answer questions inaccurately, often influenced by the wording of questions.
Reliability
The consistency of a measure or test across time and contexts.
Validity
The extent to which a test measures what it is purported to measure.
