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The probability that the test correctly rejects the null hypothesis when the alternative hypothesis is true. For example, a test with 80% power has an 80% chance of correctly detecting an effect.

A statement that proposes there is an effect or a difference. For example, the alternative hypothesis might claim that a new drug is effective in treating a disease.

A hypothesis test that checks for statistical significance in both directions of a distribution. For example, testing if a drug is either more or less effective than the existing one.

The threshold significance level at which the null hypothesis is rejected. It's the probability of committing a Type I error. For instance, an alpha level of 0.05 implies a 5% risk of wrongly rejecting the null hypothesis.

A quantitative measure of the magnitude of an experimental effect. For example, Cohen's d is an effect size indicating how many standard deviations two means differ by.

A statistical measure that expresses how likely a result is not due to chance alone. For example, a p-value less than 0.05 typically indicates statistical significance.

A statement that there is no effect or no difference, and any observed effect is due to sampling or experimental error. For example, the null hypothesis might state that a drug has no effect on a disease.

An error that occurs when the null hypothesis is rejected when it is actually true. For example, stating there is an effect when there is none, with a probability equal to the significance level, typically denoted as alpha.

The number of observations or datapoints used in a statistical analysis. For example, a larger sample size can lead to more reliable results and less sampling error.

The difference between a sample statistic and the true population parameter it estimates. For example, the difference between the sample mean and the population mean.

An adjustment to p-values when multiple comparisons are made, to reduce the chance of a Type I error. For instance, dividing the alpha level by the number of tests to get a new threshold.

A range around a sample statistic that estimates the range of values within which the true population parameter lies, based on a given confidence level. For example, a 95% confidence interval means we can be 95% certain the interval contains the true parameter.

An error that occurs when the null hypothesis is not rejected when it is actually false. For example, stating there is no effect when there is, with a probability denoted as beta.

The probability of observing a statistic as extreme as, or more extreme than, the observed one, assuming the null hypothesis is true. For example, a p-value of 0.03 suggests a 3% chance of seeing the observed result if the null hypothesis is correct.