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Before conducting inferential statistics, researchers must thoroughly understand their data at a descriptive level to clearly see 'what happened' in their study. Even though inferential statistics are required for formal reporting, descriptive statistics alone can provide strong preliminary evidence about whether a treatment worked or if variables are unrelated.

Understanding Descriptive Statistics

In a study comparing two groups, the treatment group ($$n = 50$$) has a mean score of $$34.32$$ ($$SD = 10.45$$) and the control group ($$n = 50$$) has a mean score of $$21.45$$ ($$SD = 9.22$$), resulting in an extremely strong Cohen's $$d$$ of $$1.31$$. These descriptive statistics alone clearly indicate that the treatment was effective, although inferential statistics (like a $$t$$-test) are still required for a formal research report.

Example of Descriptive Statistics Showing an Effect

In a correlational study, a scatterplot reveals an indistinct 'cloud' of data points, and the calculated Pearson's $$r$$ is a trivial $$-.02$$. These descriptive statistics alone make it clear that the variables are essentially unrelated, even though inferential statistics are still required for the formal research report.

Example of Descriptive Statistics Showing No Effect

Inferential statistics are a set of mathematical techniques used by researchers to determine whether a statistical result or relationship observed in a study's sample reflects something that is genuinely true of the broader population.

Inferential Statistics

Before conducting inferential statistics, researchers must thoroughly understand their data at a ____ level to clearly see 'what happened' in their study.

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