Type I error

John Jenkins, is performing a study on the behavior of the mean P/E ratio for a sample of small-cap companies. Which of the following statements is most accurate?

A) One minus the confidence level of the test represents the probability of making a Type II error. B) A Type I error represents the failure to reject the null hypothesis when it is, in truth, false. C) The significance level of the test represents the probability of making a Type I error.


You’re asking this question because . . . ?

just asking to get an explanation bro.

It’s definitional… your chosen significance level (alpha) is the probability of making a Type I error. You select a significance level, alpha, when conducting a hypothesis test. This is your threshold for making a Type I error (rejecting Ho when Ho is true). Say you select a 1% significance level; you’re saying that you won’t accept more than a 0.01 probability of incorrectly rejecting the null hypothesis for the test. If the p-value is greater than this, you fail to reject the null hypothesis. If the p-value for the test is less than (or equal to 0.01), you reject the null hypothesis.

thanks tickersu…you know, sometimes i ask some idiotic questions, because the more i am getting into the materials the more confused i am becoming. some topics i am comfortable with , but in some topics like "hypothesis testing "my knowledge is complete zero.

It isn’t idiotic. As you said, you just don’t have a lot of background with it-- and there’s nothing wrong with that. Glad to help!

Thanks tickersu.