Inferential Statistics
Techniques that use sample data to draw conclusions about a wider population, such as hypothesis tests and confidence intervals.
Inferential statistics let you generalize from a sample to the population it was drawn from. Because a sample never mirrors the population exactly, these methods quantify the uncertainty involved. Common tools include hypothesis tests (t-tests, ANOVA, chi-square), confidence intervals, and regression models. Each produces estimates together with a measure of how much sampling error could explain the result.
Valid inference depends on how the sample was obtained and whether the chosen test's assumptions hold, which is why random sampling and assumption checks matter so much in quantitative work.
In a thesis, inferential statistics are how you answer research questions and test hypotheses. Choosing the right test, reporting p-values with effect sizes and confidence intervals, and stating assumptions explicitly are things examiners check closely, and they separate a defensible results chapter from a merely descriptive one.
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