Question

A Type I statistical error occurs when a researcher claims that there is not enough support to establish significance for the research hypothesis, but in fact there is enough.

True or false?

Answer 1

I believe the answer is: enough statistical support for the research hypothesis when there is not
In statistic terms, a type I error refers to the occurrence of "false positive" findings.
A false positive often happen when we do not have enough subjects which make us believe the data that we took from a small sample represent the true condition outside the research.

Answer 2

False.

Step-by-step explanation:

If you reject the null hypothesis when it is true, you make a type I error. An example can help you understand this type of statistical error well:

A medical researcher wants to compare the effectiveness of two medications. The null and alternative hypotheses are:

Null hypothesis (H0): μ1 = μ2 (Both medications have the same efficacy).

Alternative hypothesis (H1): μ1 ≠ μ2 (The two medications do not have the same efficacy).

A type I error occurs if the researcher rejects the null hypothesis and concludes that the two medications are different when they are not.

By comparing the exact definition of Type I Error and the example shown with the statement presented in the question, we conclude that this statement is false, as it does not match the true concept of that type of error.

Type I error is also called false positive, which is the error that is incurred when the researcher rejects the so-called null hypothesis (that hypothesis that is created with the mission of rejecting and therefore supporting an alternative hypothesis), being in effect and on the contrary valid the same in the population studied.

Then, the statement is False.

Hope this helps!