StatReasonResources
These pages answer the concept and interpretation questions that come up before or after using a calculator: what a result actually means, which method fits your data, and what a number does not tell you. None of them repeat a calculator's own workflow.
- What a P Value Does and Does Not Mean
Read a p-value correctly: what it answers, what it never claims, and the order to read a result in.
Guide - P Value Misconception Checker
Classify six common statements about p-values as safe, unsafe, or incomplete, with a reason for each.
Interactive - How to Interpret a Confidence Interval
Understand what a confidence interval actually claims, why width matters, and what the confidence level means.
Guide - Confidence Interval vs Prediction Interval
See why a prediction interval for one new point is always wider than a confidence interval for the mean response.
Visual - Effect Size and Why Magnitude Matters
Distinguish a raw difference, a standardized effect, an association measure, and a practical importance threshold.
Guide - Statistical Significance vs Practical Significance
See how a fixed effect can become statistically significant purely by increasing sample size.
Visual - Choose the Right Statistical Test
Answer a few visible questions about your data and design to find the one approved test that matches.
Interactive - Paired vs Independent Data
Tell whether your data is genuinely paired or independent, and why that decision changes which test is valid.
Guide - What a Nonsignificant Result Means
Understand why a p-value above alpha means inconclusive evidence, never proof that no effect exists.
Guide - Assumptions in Statistical Testing
Learn the difference between what a test calculates, what evidence you should review, and what only you can verify.
Guide - Normality Tests and What They Can Tell You
See how a Q-Q plot, Shapiro-Wilk, and Anderson-Darling each respond to normal-like and skewed samples.
Visual - Outliers Need Investigation Not Automatic Deletion
Learn how to investigate a flagged outlier responsibly instead of deleting it automatically.
Guide - Mean vs Median When Data Are Skewed
Toggle one extreme value on and off to see how much more it moves the mean than the median.
Visual - Sample vs Population Standard Deviation
Understand why the sample and population standard deviation use different denominators, and when each applies.
Guide - Pearson vs Spearman Correlation
Compare four fixed scatter patterns to see when Pearson and Spearman correlation agree and when they diverge.
Interactive - Correlation Does Not Prove Causation
See the specific alternative explanations a correlation alone can never rule out.
Guide - How to Interpret Regression Results
Read a regression coefficient, interval, and prediction the way the model actually supports, not further.
Guide - Residual Plots and Regression Diagnostics
See how curvature, unequal spread, and high leverage each look different in a residual plot, with a text alternative.
Visual - R Squared and Adjusted R Squared Explained
Learn what R-squared measures, why adjusted R-squared exists, and what neither one proves.
Guide - ANOVA Results and Post Hoc Tests
Read an ANOVA omnibus result correctly and know when and how to follow up with a post hoc test.
Guide - Multiple Comparisons and False Positives
See exactly how the chance of a false positive grows with family size, and how a correction controls it.
Interactive - Parametric vs Nonparametric Tests
Understand what parametric tests assume, what rank-based alternatives trade away, and when each fits.
Guide - Statistical Power, Sample Size, and Minimum Detectable Effect
See how alpha, power, effect size, and sample size trade off, and why post hoc power is not useful.
Interactive - Odds Ratio vs Risk Ratio vs Risk Difference
See the same 2x2 event data expressed as an odds ratio, a risk ratio, and a risk difference side by side.
Interactive - How to Interpret a Proportion Confidence Interval
Understand why a Wilson interval is used by default and how to read it, including at zero observed events.
Guide - Missing Data Before Analysis
Learn why a missingness count alone cannot tell you the mechanism behind the missing values.
Guide - Bootstrap Confidence Intervals Explained
Watch a small, fixed-seed resampling animation to see exactly what a bootstrap interval is built from.
Visual - Permutation Tests Explained
Watch group labels shuffle to see how a permutation test builds its null distribution from your own data.
Visual - Reliability vs Agreement
Understand the real difference between measurement reliability and rater agreement, and why neither implies validity.
Guide - ICC vs Kappa vs Bland Altman
Answer a few visible questions about your data type and rater count to find the matching reliability measure.
Interactive - How to Report Statistical Results
Build a method-aware reporting checklist from your own results, in the order that avoids common overclaims.
Interactive - Reading a Statistical Result in Order
See the right order to read a result: estimate, uncertainty, magnitude, test, assumptions, then next step.
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