Descriptive vs Inferential Statistics

Statistics in research serves two fundamental purposes. Descriptive statistics describe what your data look like. Inferential statistics use your data to make claims about a broader population. Descriptive statistics include measures of central tendency (mean, median, mode), measures of spread (standard deviation, interquartile range, range), frequencies and percentages, and visual displays (histograms, box plots, bar charts). Table 1 in most clinical papers is pure descriptive statistics — it summarizes the study population. Inferential statistics go beyond description. They use sample data to estimate what is likely true in the larger population, accounting for the uncertainty inherent in sampling. Key inferential tools include confidence intervals (estimating a range for the true population value), p-values (testing whether an observed difference is likely due to chance), and hypothesis tests (formal decisions about the null hypothesis). The distinction matters because descriptive statistics alone cannot answer research questions. Saying 'the treatment group had a lower infection rate' is descriptive. Saying 'the treatment group had a significantly lower infection rate (RR 0.6, 95% CI 0.4-0.8, p=0.002)' is inferential — it claims the difference is unlikely due to chance and estimates the true effect. Every research paper uses both types. You describe your sample (Table 1) and then infer conclusions about the population (Results section). Understanding the distinction helps you read papers critically and plan your own analyses.