Statistics Calculator
Descriptives • ANOVA • χ² tests • distributions • Bayesian updates — local-first.
Data A
Tip: supports % (e.g., 12%) and simple fractions (e.g., 3/4).
Data B (optional)
Use for comparisons/correlation. Leave empty if not needed.
Histogram bins
Mean CI level
One-sample μ₀
H₀: mean(Data A) = μ₀
Two-sample test
Requires Data A and Data B.
Correlation
Pairs by index (min length).
Groups input
One-way ANOVA (k≥2). Reports F, p-value, η² effect size, and ANOVA table.
Independence test: contingency table
Reports χ², df, p-value, expected counts, Cramer's V.
Goodness-of-fit: observed & expected
If expected sums to ~1, treated as probabilities; otherwise treated as expected counts.
Distribution
k / x
Quantile p
μ
σ
n
p
λ
Computes PDF/PMF, CDF, and quantile. Also plots the distribution curve (approx).
Model
Credible level
Prior α
Prior β
Successes
Trials
Bayesian updates are conjugate and compute closed-form posteriors + credible intervals.
REPORT
Ready
Warning: Statistical tools can be misused. Validate assumptions (independence, normality, sample size) and verify results before decisions.
How the Statistics Calculator works
Paste a dataset and GetCalcMaster computes common summary statistics and visualizes the distribution. Use it to learn and sanity-check calculations — then validate assumptions before real-world decisions.
Note: Educational use only. Statistical outputs can be misinterpreted if assumptions (independence, measurement quality, sample size, distribution shape) don’t hold. Always verify.
Quick steps
- Paste or enter your dataset (numbers) in the requested format.
- Select the statistic or test you want to compute.
- Review the result and interpret it in context (units, assumptions, sample size).
- Record methodology and inputs in Notebook so you can reproduce the calculation later.
- Enter values separated by commas, spaces, or new lines.
- The tool computes descriptive metrics (like mean/median/spread) and renders a quick chart.
- Results update live as you edit the dataset.
- Use history to compare datasets and keep a record while exploring.
Data formatting tips
- Use a single column of numeric values (no units mixed into numbers).
- If you have missing values, remove them before computing statistics.
- Large datasets may be slower — consider sampling for quick exploration.
Interpreting results
Descriptive statistics summarize your data but do not prove causality.
If you’re doing inference, confirm assumptions and use appropriate tests and confidence intervals.
Privacy
Dataset calculations run locally in your browser.
Clear history to remove stored entries from this session.
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