A Type I error occurs when the null hypothesis is rejected when it is true. A Type II error occurs when the null hypothesis fails to be rejected when it is false.
A Type I Error is defined as rejecting the null hypothesis when it is actually true. The probability of committing a Type I error is the significance level or alpha risk.
The researcher must state the hypotheses prior to the collection and analysis of the data. More importantly, it is necessary to know the hypotheses before selecting the appropriate test statistic.
AIM 16: Define, calculate and interpret the chi-squared test of significance.
1、You have collected monthly returns for a mutual fund and want to test the null hypothesis that the standard deviation exceeds the advertised standard deviation of 3.5 percent. The most appropriate test statistic is based on a:
AIM 17: Define, calculate and interpret the F-test of significance.
1、In order to test if Stock A is more volatile than Stock B, prices of both stocks are observed to construct the sample variance of the two stocks. The appropriate test statistics to carry out the test is the: