Descriptive vs Inferential Statistics

Statistics has two branches that answer different questions. Descriptive statistics summarise the data you actually collected; inferential statistics use a sample to draw conclusions about a wider population.

1. Descriptive statistics — summarise what you have

They describe your dataset with measures of central tendency (mean, median, mode), measures of spread (range, variance, standard deviation), and frequencies or percentages, often shown in tables and charts. They describe your data — they do not, by themselves, generalise beyond it.

2. Inferential statistics — generalise to a population

Using a sample, these estimate or test claims about the population through hypothesis tests and confidence intervals. Common tools include t-tests, ANOVA, chi-square, correlation, and regression — chosen to match your data type and design.

3. What a p-value really means

A p-value is the probability of obtaining results at least as extreme as yours if the null hypothesis were true. It is not the probability that the null hypothesis (or your hypothesis) is true. A small p-value (below a pre-set level, often 0.05) means such a result would be unlikely under the null — that is all.

4. Significance is not importance

A "statistically significant" result can still be tiny and unimportant. Always report an effect size and a confidence interval alongside the p-value, so readers see the size and precision of the effect, not just whether it passed a threshold.

5. Which do you need?

Almost every study reports descriptive statistics first. Add inferential statistics only when you need to generalise from a sample to a population — and make sure the test's assumptions fit your data.

How ProSearch helps

ProSearch gathers studies on your topic and how they analysed their data, so you can see which statistical approaches are standard in your field before you plan your own analysis.