In a hypothesis test, a type I error occurs when you reject a null hypothesis that is actually true. In other words, a statistically significant test result suggests that a population effect exists but, in reality, it does not exist. The difference you observed in the sample is the product of random sample error.

The probability of committing a type I error equals the significance level you set for your hypothesis test. A significance level of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for alpha. However, if you use a lower value for alpha, you are less likely to detect a true difference if one really exists.

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