Eleisha's Segment 12: P-Value Tests
1. What is the critical region for a 5% two-sided test if, under the null hypothesis, the test statistic is distributed as ? That is, what values of the test statistic disprove the null hypothesis with p < 0.05? (OK to use Python, MATLAB, or Mathematica.)
Let t = the value of the test statistic If the test statistic is distributed as , then for a two sided test the critical region is when . This can calculated by taking the inverse of the CDF of the probability distribution and and evaluating it at (1 - 0.05/2).
2. For an exponentially distributed test statistic with mean (under the null hypothesis), when is the the null hypothesis disproved with p < 0.01 for a one-sided test? for a two-sided test?
Let t = the value of the test statistic The pdf for an exponentially distributed test statistic with parameter is:
We can solve for the critical region in a similar manner to question one by determining the inverse of the CDF of p(x) and evaluating it a (1 - 0.01) for a one sided test and (1 - .01/2). For a one -sided test the null hypothesis is disproved with p< 0.01 when t> 4.60517. For a one -sided test the null hypothesis is disproved with p< 0.01 when |t|> 4.60517.
To Think About:
1. P-value tests require an initial choice of a test statistic. What goes wrong if you choose a poor test statistic? What would make it poor?
2. If the null hypothesis is that a coin is fair, and you record the results of N flips, what is a good test statistic? Are there any other possible test statistics?
3. Why is it so hard for a Bayesian to do something as simple as, given some data, disproving a null hypothesis? Can't she just compute a Bayes odds ratio, P(null hypothesis is true)/P(null hypothesis is false) and derive a probability that the null hypothesis is true?
Back To: Eleisha Jackson