Any time you reject the null hypothesis, there is some probability that you could be mistaken.

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This is because the null hypothesis is assumed to be true until proven otherwise, so if you reject it based on a sample or experiment, there is always a chance that the result was due to chance or error rather than a true effect in the population. This probability is typically calculated as the p-value, which represents the probability of observing a result as extreme or more extreme than the one obtained, assuming that the null hypothesis is true. A commonly used significance level or alpha level is 0.05, meaning that if the p-value is less than or equal to 0.05, the result is considered statistically significant and the null hypothesis is rejected. However, this does not guarantee that the result is true or replicable, and there is always a chance of making a Type I error (false positive) or a Type II error (false negative) in hypothesis testing.

Any Time You Reject the Null Hypothesis, There Is Some