Descriptive Statistics Calculator

When to use

• Use it to inspect the center, spread, range, quartiles, and shape indicators of one numeric sample.
• Use it before inferential analysis to identify missing values, unusual observations, and scale problems.
• Use separate grouped or model-based methods when observations have weights, clusters, or repeated measures.

How to use

1. Paste one numeric sample or load and select one local CSV column.
2. Choose sample or population variance according to what the values represent.
3. Calculate the summary and inspect the ordered values, histogram, quartiles, and warnings.
4. Recheck source records before removing or changing any unusual value.

Inputs

Numeric values

One finite quantitative observation per token or selected CSV cell.

Variance convention

Sample variance divides by n-1; population variance divides by n.

Histogram bins

The display grouping changes the visual shape but not the underlying observations.

CSV column

A selected numeric column parsed locally after the file is loaded.

Notation

Interpretation

• Mean and median describe center differently when the distribution is skewed.
• Standard deviation and IQR describe different aspects of spread and should retain the variable's units.
• An outlier flag requests source and domain review; it does not prove an observation is erroneous.

Effect-size guidance

• Compare spread with the measurement scale and expected domain variation.
• Report n and units beside every summary.
• Use a distribution plot because equal means and standard deviations can conceal different shapes.

Worked examples

Assumptions

• Inputs are finite numeric values after parsing, with missing or invalid values handled according to the selected workflow.
• The selected variance and quartile conventions match the reporting context.
• Values are comparable measurements from the same intended variable.

Practical guidance

• Preserve the raw sample and document exclusions.
• Check whether zeros are measurements or missing-value codes.
• Stratify summaries only when the grouping variable and analysis plan justify it.

Common mistakes

• Treating descriptive statistics as a distribution proof.
• Comparing sample and population standard deviation without checking the selected convention.

Limitations

• Mean and standard deviation are sensitive to outliers.
• Type inference and missing-value settings affect CSV results.

Related workflow links