Applied statistics connects data collection to estimates and decisions under uncertainty. A calculation is meaningful only after the population, estimand, sampling process, and assumptions are specified.
Choose a starting point
Begin with summaries and distributions. Continue through intervals, tests, regression, study design, and diagnostics while keeping the actual question visible. Practice on datasets where missingness and selection matter.
- Applied Statistics Tutorial
- Population, estimand and sampling frame: name the quantity before calculating
- Hypothesis tests: pair the decision rule with an effect size
- Linear regression slopes: define the comparison a coefficient makes
- Paired comparisons: analyze within-unit changes and preserve the match
- Prior sensitivity: show when limited data leave a decision exposed
- Stratified survey estimates: weight toward the named population
- Rare proportions: keep interval uncertainty visible at zero and one
- Minimum detectable effect: plan a study around a useful change
Common Mistakes
A narrow confidence interval may measure the wrong population precisely. The linked lessons show where design choices, dependence, and model assumptions change interpretation.