Descriptive statistics summarize a dataset into key indicators: central tendency, dispersion, and distribution shape. It is the essential starting point of any statistical analysis.
When to use it
Explore a new dataset before any analysis
Describe a sample in a report or publication
Detect outliers or skewed distributions
Compare characteristics across multiple variables
Requirements
Continuous or ordinal variables
No distribution assumption required
What StatsLab computes
Mean, median, mode
Standard deviation, variance, standard error
95% CI of the mean
Minimum, maximum, quartiles (Q1, Q3)
Skewness and kurtosis
Histogram, box plot, Q-Q plot, violin plot
Bar chart (Mean ± SD)
Worked example
Context : A researcher measures pain scores (0–10) in 50 patients before treatment.
Result : Mean = 6.2 · Median = 6.5 · SD = 1.8 · Min = 2 · Max = 10
Interpretation : The distribution is slightly left-skewed (median > mean). Most patients report moderate to severe pain. The 95% CI [5.7; 6.7] estimates the population mean pain level.