Practice Datasets

Eighty volunteers were randomly assigned to a normal night's sleep or a night restricted to four hours, then took a 30-minute vigilance task the next morning. The outcome is the number of attention lapses (errors). Did sleep restriction hurt performance, and by how much?

Try these

1. Run an independent-samples t-test comparing errors across the two conditions.
2. Report and interpret Cohen's d: the study was built around a medium effect (check it with the effect-size converter).
3. Before trusting the p-value, check the assumptions (normality, equal variances).

Three groups of 35 students revised for the same exam using one of three techniques (rereading, flashcards, or practice testing), then sat the exam (scored 0–100). Which method wins? And notice the spread differs by group, so this doubles as a homogeneity-of-variance workout.

Try these

1. Run a one-way ANOVA across the three methods.
2. Check equal variances (Levene's test). The SDs are deliberately unequal.
3. Follow up with post-hoc tests (e.g. Tukey) to see which methods actually differ.

Sixty people reported their average daily screen time and rated their sleep quality (0–10). There's a real negative relationship in here, but one person (row 60) reports 11.5 hours of screen time and excellent sleep, sitting far off to the right. This dataset is really about what one influential point can do.

Try these

1. Plot it, then compute Pearson's r. Always look before you trust a coefficient.
2. Fit a simple regression and inspect leverage & influence (Cook's distance) for row 60.
3. Recompute r without that point, then decide what to do with it, and report both.

A classic 2×2 memory experiment: participants encoded a word list either shallowly or deeply, then were tested in the same room or a different one (25 per cell). The number of words recalled hides a genuine interaction: context only matters when encoding was deep.

Try these

1. Run a two-way factorial ANOVA. You want the encoding × context interaction.
2. Draw the interaction plot of the four cell means (non-parallel lines = interaction).
3. Put the interaction into words: the effect of context depends on encoding depth.

An 8-item wellbeing questionnaire (each item 1–5). Two items are negatively worded and must be reverse-coded before scoring: q3 and q6. A few cells are blank (real surveys have missing data). Practice reverse-coding, building a composite, and checking the scale's reliability.

Try these

1. Reverse-code q3 and q6 (new = 6 − old), then compute a composite (mean of the 8 items).
2. Assess the scale with Cronbach's α.
3. Decide how to handle the missing values (listwise vs pairwise) and note how many cases each keeps.

A deliberately dirty clinic intake sheet, built for the Data course. It hides duplicates, impossible values, a unit mix-up, inconsistent category spellings, and three different date formats. Your job is to find every problem before you analyze anything, and to never edit the raw file, only a cleaning script.

Try these

1. Find and document every data-quality problem. How many can you spot before reading the solution?
2. Build a reproducible cleaning workflow: de-duplicate, standardize sex, fix the units, parse the dates.
3. Write the validation rules (allowed ranges, allowed categories) that would have caught each error at entry.

Sixty athletes were measured on a strength lift (1RM, kg) at weeks 0, 6, and 12, split between a training program and a control group. The data is in wide format (one row per athlete, one column per timepoint), perfect for practicing reshaping and repeated-measures analysis.

Try these

1. Pivot wide → long so each row is one athlete-at-one-time (R/JASP usually want long).
2. Run a repeated-measures / mixed ANOVA with time (within) and group (between).
3. Interpret the group × time interaction: does the program gain more than the control?

Records for 160 graduate applicants: undergraduate GPA, a GRE-style test score, whether they had research experience, and the binary outcome: admitted or not. A binary outcome means logistic regression, not linear.

Try these

1. Fit a logistic regression: admitted ~ gpa + gre_score + research.
2. Interpret the odds ratios (convert them from the coefficients; the effect-size tool handles OR too).
3. As a warm-up, compare admit rates by research with a chi-square test on the 2×2 table.