The correct answer is C), so all statements are correct. But I am almost certain that II and III are not correct (or should be switched). It was my understanding that the chi-square distribution is used to test whether two characteristics from a population are independent of each other and the F-Test is used to determine if the variances of two populations are the same. Alternatively, we can use the Chi-Square distribution to test if the variance of a population is equal to a certain value.
Firstly, while this question may be good for generally testing your knowledge, CFA Institute specifically mentions that these types of answer choices will not appear on the actual exam ! See first paragraph under bold heading “Answer Choices” of the document in the link below.
The curriculum uses the F test when testing the equality/inequality of two population variances and the chi-squared test for a test concerning a single population variance (all populations are assumed to be normally distributed for these tests)
So I personally wouldn’t bother reading too much into these answers. But for the record, I think A) is correct.
I wouldn’t really say that I is okay. It’s a well known guideline to never accept the null hypothesis; you either reject or fail to reject the null. The tests we conduct can’t ever prove the null true (leading to acceptance).
It’s perfectly acceptable if they change the wording from “accept” to “reject”.
I agree. I’ve heard (no real personal experience) that Investopedia isn’t very accurate with the CFA exam material (and some non-CFA topics), which is a shame to hear about a free resource.
IV also doesn’t look very nice. I may be reading it wrong, but a large sample would allow us to use a normal distribution (irrespective of the true distribution’s shape) based on the central limit theorem (i.e. the sample only needs to be large, not also from a normal distribution).
Also, III somewhat looks like it should be a Chi-Squared test to determine the independence of two qualitative variables (for example, determining if coffee drinking status (Yes/No) is infuenced by gender (Male/Female)). You could test this using a Chi-squared test for heterogeneity/independence. Granted, this is beyond the curriculum, but maybe it’ll give some context to what they might mean by two “characteristics” being independent of one another. A finance context: does the bond issue (government vs non-government) influence the default status on an issue (defaulted vs not)?
I agree about the quality of questions on Investopedia. Regarding the “acceptance of the null”: I worked my may through the entire bank of Quantitative Questions in their CFA section, and I think it is safe to say, that you won’t be able to reject the hypothesis that they never ‘fail to reject the null’ but rather always ‘accept the null’, so by now I have come to terms with their wording (it used to drive me nuts). But in any case, they still have a large question bank on all sections so I figure it cannot hurt.
Yes (sorry about the lack of clarity in my statement). It was pounded in my head in countless statistics and econometrics classes to ‘fail to reject the null’, so as I said, I just started to ignore the ‘accepting the null’ in their questions. But if this is the primary source of preparation for anyone, it will take a while to get that out of their heads. Here is a sample of questions (which is obviously not representative, you have to take my word for it):
A CEO of a large corporation wishes to examine the relationship between her company’s sales and GDP. Looking at the “monthly” data over the last five years, she determines that the correlation coefficient between company sales and the economy is 0.53. With a level of significance of 5%, is it possible that there really is no correlation between company sales and GDP? A) Since t-calc of 4.76 falls outside the critical t-values, the statement that company sales are not correlated with GDP should be accepted. B) Since t-calc of 1.08 falls outside the critical t-values, the statement that company sales are not correlated with GDP should be rejected. C) Since t-calc of 4.76 falls outside the critical t-values, the statement that company sales are not correlated with GDP should be rejected. D) Since t-calc of 1.08 falls within the critical t-values, the statement that company sales are not correlated with GDP should be accepted.
I see what you mean. Luckily, your prior education taught you the correct phrase of “fail to reject the null.” People who tell you there is no difference between the statements either don’t really know what it means or are being very careless.