What is Type I error?

What is Type I error?

• A. Failing to detect a real effect
• B. Detecting an effect when none exists
• C. Biased estimate of the mean
• D. Random sampling error

Answer: (B)

Type I error is a false positive in hypothesis testing: you conclude there is an effect when there actually isn’t one. This happens when you reject the null hypothesis even though it is true. The chance of making this error is the alpha level you set (often 0.05). For example, you might conclude a new drug works when it doesn’t. This isn’t the same as failing to detect a real effect (that’s a Type II error), nor is it about a biased estimate of the mean or about random sampling error, which are different issues related to estimation or variability rather than the decision rule about the null hypothesis.

Type I error is a false positive in hypothesis testing: you conclude there is an effect when there actually isn’t one. This happens when you reject the null hypothesis even though it is true. The chance of making this error is the alpha level you set (often 0.05). For example, you might conclude a new drug works when it doesn’t. This isn’t the same as failing to detect a real effect (that’s a Type II error), nor is it about a biased estimate of the mean or about random sampling error, which are different issues related to estimation or variability rather than the decision rule about the null hypothesis.