A Type I error, also known as alpha error, happens when the null hypothesis is actually true in the population but the sample evidence leads the researcher to reject it. In other words, the test concludes that an effect or difference exists when in reality it does not. The probability of committing this error is controlled by the chosen significance level, such as 0.05.
Option A:
Option A describes failing to reject a false null hypothesis, which is a Type II error (false negative), not Type I.
Option B:
Option B matches the formal definition of Type I error: incorrect rejection of a true null hypothesis, leading to a false positive conclusion.
Option C:
Option C refers to accepting (or failing to reject) a true null hypothesis, which is the correct decision and not an error.
Option D:
Option D portrays correct rejection of a false null hypothesis, which is the desired outcome of hypothesis testing rather than an error.
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