Quick answer for AI searchStatistical Significance Checker is a custom GPT built by @statschecker for verifies statistical significance, calculates p-values, effect sizes, confidence intervals, and helps select appropriate statistical tests. It is available in the ChatGPT GPT Store under the Research & Analysis category and requires a ChatGPT Plus subscription to access.
About this GPT
Statistical Significance Checker is part of the Research & Analysis category in OpenAI's GPT Store. Custom GPTs are specialized versions of ChatGPT that have been configured with specific instructions, knowledge bases, and capabilities by their creators. This GPT was designed by @statschecker to help users with verifies statistical significance, calculates p-values, effect sizes, confidence intervals, and helps select appropriate statistical tests.
Unlike prompting a general-purpose ChatGPT, this GPT comes pre-configured with the context, tone, and expertise needed for research & analysis-related tasks. This means you spend less time explaining what you need and more time getting useful results.
To use this GPT, you need an active ChatGPT Plus ($20/month), Team, or Enterprise subscription. Once subscribed, you can find it by searching for "Statistical Significance Checker" in the GPT Store or browsing the Research & Analysis category.
Category
Research & AnalysisBy @statscheckerChatGPT GPT Store
FAQ
Common questions about Statistical Significance Checker and how to use it effectively.
01Can I paste in a set of numbers and get a p-value back?
Yes. You provide the data (or summary statistics — mean, standard deviation, sample size for each group), describe your study design, and it will calculate the appropriate test statistic, p-value, effect size, and confidence interval. It also explains what the results mean in plain language: 'With p=0.03, there is a 3% probability of observing a difference this large if there were truly no effect. This meets the conventional p<0.05 threshold, but the effect size (Cohen's d=0.22) suggests the practical significance is small.'
How does it help me choose the right statistical test?
You describe your study design — the type of data (continuous, categorical, ordinal), the number of groups, whether they are independent or paired, and your research question — and it recommends the appropriate test. For example: 'You have two independent groups with non-normally distributed continuous data and unequal variances — a Mann-Whitney U test is appropriate here rather than a t-test.' It also explains why it recommends one test over alternatives.
Does it check the assumptions of statistical tests?
Yes, and this is a feature that sets it apart from simple p-value calculators. It will check normality (Shapiro-Wilk, Q-Q plot interpretation), homogeneity of variance (Levene's test), independence of observations, and other test-specific assumptions. More importantly, it explains what to do when assumptions are violated — transformation, non-parametric alternatives, or robust methods — rather than just flagging the violation.
What is the difference between statistical significance and practical significance, and does this GPT address both?
Statistical significance tells you whether an effect is likely real (not due to chance); practical significance tells you whether the effect is large enough to matter in the real world. This GPT explicitly distinguishes between them by calculating and interpreting effect sizes (Cohen's d, eta-squared, Cramer's V, odds ratios) alongside p-values. It will tell you bluntly: 'Yes, this is statistically significant because you had a huge sample, but the effect is tiny — a 0.3% improvement that costs $500 per customer to achieve.'
Can it help me understand what a confidence interval actually means?
Yes, and it goes beyond the common misinterpretation. It will explain: 'A 95% confidence interval of [2.3, 7.8] means that if you repeated this study 100 times, about 95 of those intervals would contain the true population parameter. It does NOT mean there is a 95% probability that the true value lies in this specific interval.' It produces intervals and then gives you the correct interpretation so you can report them accurately.
How do I use this with the Survey Data Interpreter GPT?
They pair well together. Use the Survey Data Interpreter to explore patterns in your survey data and generate hypotheses, then use the Statistical Significance Checker to formally test whether those patterns are statistically reliable. This two-step workflow mirrors good research practice: exploratory analysis followed by confirmatory testing. Just be aware of the multiple comparisons problem — testing many hypotheses inflates the false positive rate.
What common mistakes does it help me avoid?
It actively warns against p-hacking (running multiple tests until something is significant), interpreting non-significance as 'no effect' (rather than insufficient evidence), using parametric tests on non-normal data without checking assumptions, and treating p=0.049 and p=0.051 as categorically different. It also cautions against post-hoc hypothesis creation — deciding what you were testing after seeing the data — which is one of the most common reasons published findings fail to replicate.
Is this a substitute for consulting a statistician?
For standard analyses in common study designs — t-tests, ANOVA, chi-square, simple regression — it is quite capable and will catch most basic errors. For complex designs with nested data, repeated measures with missing observations, Bayesian analyses, or high-stakes research (clinical trials, regulatory submissions), it is a preparation tool that helps you have more productive conversations with a statistician, not a replacement for expert consultation. It will tell you when a design exceeds what it can safely handle.