B. Research Methods and Statistical Analysis Key Terms

Confounding Variable

A variable that unintentionally affects the outcome, making it unclear whether the independent variable caused the result

Random Assignment

Placing participants into groups by chance to reduce bias and ensure groups are similar

Experimental Designs

Different ways to structure experiments (e.g., between-subjects, within-subjects, mixed designs)

Experiments vs Surveys

Experiments manipulate variables to find cause-and-effect; surveys collect self-reported data without manipulation

Descriptive Statistics

Statistics that summarize and describe data (e.g., mean, median, mode)

Inferential Statistics

Statistics used to make predictions or generalizations about a population based on sample data

Central Tendency

Measures that represent the center of data (mean, median, mode)

Dispersion

Measures that show how spread out data is (range, variance, standard deviation)

Variance

A measure of how far each value is from the mean, squared

Standard Deviation

The average distance from the mean; useful for understanding spread in normal distributions

Distributions

The shape of data (e.g., normal, skewed) which affects how we interpret averages

Sampling Error

The difference between a sample result and the true population value due to chance

Type I Error

False positive (rejecting a true null hypothesis)

Type II Error

False negative (failing to reject a false null hypothesis)

Confidence Interval

A range of values likely to contain the true population parameter

Effect Size

The strength or magnitude of a relationship or difference

Statistical Significance

Indicates whether a result is likely due to chance (often p < .05)

One-Tailed Test

Tests for a relationship in one specific direction

Two-Tailed Test

Tests for a relationship in both directions

Chi-Square Test Use

Used to examine relationships between categorical variables

Chi-Square Interpretation

Determines if observed differences are different from expected by chance

Chi-Square & Hypothesis

If significant, reject the null and support the experimental hypothesis

t-Test Use

Used to compare the means of two groups

t-Test Interpretation

Shows whether the difference between two means is statistically significant

t-Test & Hypothesis

If significant, reject the null and support the experimental hypothesis

ANOVA Use

Used to compare means of three or more groups

ANOVA Interpretation

Indicates whether at least one group differs significantly

ANOVA & Hypothesis

If significant, reject the null; follow-up tests identify where differences are

Correlation Use

Used to measure the relationship between two variables

Correlation Interpretation

Shows strength and direction (positive, negative, none)

Correlation & Hypothesis

If significant, supports a relationship but does NOT prove causation

Regression Use

Used to predict one variable from another

Regression Formula

y = bx + a (predict outcome using slope and intercept)

Regression Interpretation

Shows how much one variable predicts another

Regression & Hypothesis

If significant, supports prediction relationship between variables