Hypothesis Testing & Statistical Inference Calculators

Test claims about data, and estimate values with confidence intervals and margins of error.

22 calculators in this category.

Statistical inference uses a sample to draw conclusions about a larger population. A hypothesis test asks whether the data are consistent with a stated claim, and a confidence interval gives a range of plausible values for an unknown quantity.

The calculators here cover the common test statistics, critical values, and p-values, along with tools for planning a study such as power analysis. Which test applies depends on the type of data, the number of groups, and the assumptions you can reasonably make.

Calculators in this category

22 calculators

Key concepts

Null and alternative hypothesis
The null hypothesis is the default claim of no effect or no difference. The alternative is what the test looks for evidence of.
p-value
The probability of seeing a result at least as extreme as the one observed, assuming the null hypothesis is true. It is not the probability that the null hypothesis is true.
Confidence interval
A range of values calculated from a sample using a method that captures the true value in a stated proportion of repeated samples.
Statistical power
The probability that a test detects an effect of a given size when that effect is real.

Frequently asked questions

What does a p-value actually mean?

A p-value is the probability of obtaining a result at least as extreme as the one observed, if the null hypothesis were true. A small p-value indicates the data are hard to reconcile with the null hypothesis. It does not give the probability that the hypothesis is true, and it does not measure the size or importance of an effect.

Does a statistically significant result mean the effect is important?

Not necessarily. With a large enough sample, even a very small difference can be statistically significant. Look at the effect size and the confidence interval to judge whether the difference matters in practice.

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