Statistics In Psychology Course

Lesson 1 • Measurement and Scales of Data

Measurement precision determines which analyses are appropriate. Students classify variables by scale type and identify their analytical implications.

Lesson 2 • Ethical Use of Data in Psychology

Responsible data handling is inseparable from statistical practice. Students identify ethical obligations in data collection, storage, and reporting.

Lesson 3 • Probability and Chance

Probability underpins every inferential test covered later. Students calculate basic probabilities and interpret them within psychological contexts.

Lesson 4 • Research Design Fundamentals

Design choices shape what statistical conclusions are permissible. Students map experimental, quasi-experimental, and correlational designs to appropriate analyses.

Lesson 1 • Organising and Displaying Data

Raw data must be structured before analysis. Students construct frequency distributions, histograms, and stem-and-leaf plots from psychological datasets.

Lesson 2 • Effective Data Visualisation

Graphs must accurately represent psychological findings without distortion. Students select appropriate chart types and apply formatting standards for scientific publication.

Lesson 3 • Shape of Distributions

Distribution shape affects which statistics are meaningful. Students identify skewness, kurtosis, and outliers and assess their impact on summary measures.

Lesson 4 • Measures of Variability

Variability quantifies how spread scores are around the centre. Students compute range, variance, and standard deviation and link them to research interpretation.

Lesson 1 • Using the Standard Normal Table

The z-table translates scores into probabilities and percentile ranks. Students look up areas under the curve and apply them to psychological score interpretation.

Lesson 2 • Standardisation and Z-Scores

Z-scores place individual scores on a common scale. Students compute z-scores, interpret their meaning, and use them to compare across different measures.

Lesson 3 • The Normal Distribution in Depth

The normal curve models many psychological variables and sampling distributions. Students describe its properties and recognise when normality is a reasonable assumption.

Lesson 4 • Sampling Distributions and the Central Limit Theorem

The sampling distribution of the mean is the bridge to inference. Students simulate and describe how sample means distribute and why sample size matters.

Lesson 1 • Statistical Power Analysis

Power determines the probability of detecting a true effect. Students compute power, identify its determinants, and plan adequately powered studies.

Lesson 2 • Effect Sizes and Practical Significance

Effect sizes quantify the magnitude of psychological effects independent of sample size. Students compute Cohen's d, r, and eta-squared and interpret benchmarks.

Lesson 3 • Type I and Type II Errors

Errors in hypothesis testing have real consequences for psychological conclusions. Students calculate error probabilities and understand the alpha-beta tradeoff.

Lesson 4 • Significance Levels and P-Values

Alpha and p-values determine when results are deemed statistically significant. Students set alpha, compute p-values, and avoid common misinterpretations.

Lesson 1 • One-Sample t-Test

The one-sample t-test compares a sample mean to a known population value. Students apply it to test whether a psychological sample differs from a normative standard.

Lesson 2 • Assumptions and Robustness

t-Tests rest on assumptions that may be violated in practice. Students diagnose violations using plots and tests and apply corrections when needed.

Lesson 3 • Confidence Intervals for Mean Differences

Confidence intervals complement p-values by showing the range of plausible effect sizes. Students construct and interpret 95% CIs for all t-test variants.

Lesson 4 • Paired-Samples t-Test

Paired designs control individual differences by measuring the same participants twice. Students compute difference scores and apply the paired t-test to within-subjects data.

Lesson 1 • One-Way ANOVA Logic and Computation

One-way ANOVA partitions total variance into between- and within-group components. Students compute the F-ratio and link it to the omnibus null hypothesis.

Lesson 2 • Effect Sizes and Power in ANOVA

Eta-squared and omega-squared quantify ANOVA effect magnitude. Students compute these indices, compare them across studies, and plan sample sizes for ANOVA designs.

Lesson 3 • Repeated-Measures ANOVA

Repeated-measures ANOVA handles within-subjects designs with three or more time points. Students address sphericity, apply corrections, and interpret within-subjects F-ratios.

Lesson 4 • Post-Hoc Comparisons

A significant F only indicates that some means differ. Students apply Tukey, Bonferroni, and Scheffé corrections to identify which specific pairs differ.

Lesson 1 • Assumptions and Alternative Correlations

Pearson r requires linearity, normality, and no severe outliers. Students diagnose violations and apply Spearman rho or point-biserial r when appropriate.

Lesson 2 • Pearson Correlation Coefficient

Pearson r measures the strength and direction of linear association. Students compute r, test its significance, and interpret its magnitude using established benchmarks.

Lesson 3 • Simple Linear Regression Fundamentals

Regression models the predictive relationship between one predictor and one outcome. Students derive the regression equation using least-squares and interpret slope and intercept.

Lesson 4 • Regression Diagnostics

Assumption violations distort regression estimates and conclusions. Students use residual plots, Cook's D, and leverage statistics to detect and address problems.

Lesson 1 • Moderation and Interaction in Regression

Moderation tests whether a predictor's effect depends on a third variable. Students create product terms, centre predictors, and interpret interaction plots.

Lesson 2 • Standardised and Unstandardised Coefficients

Beta weights allow comparison of predictor importance on a common scale. Students convert between standardised and unstandardised coefficients and interpret each correctly.

Lesson 3 • Multiple Regression Foundations

Multiple regression extends simple regression to several predictors simultaneously. Students interpret partial regression coefficients and distinguish unique from shared variance.

Lesson 4 • Assumptions and Model Evaluation

Multiple regression assumptions extend those of simple regression. Students conduct full diagnostic checks and use fit indices to compare competing models.