You wish to test the following claim (H) at a significance level of a = 0.005. For the context of this problem, µa = µ2 - H1 where the first data set represents a pre-test and the second data set represents a post-test. 0 = Prl : °H 0 > Pr1 : "H You believe the population of difference scores is normally distributed, but you do not know the standard deviation. You obtain the following sample of data: pre-test post-test 51 49.9 50.5 20.7 57.7 37.6 33.1 29.1 53.1 25.9 64.1 55.5 46.7 51.5 64.1 46.5 What is the critical value for this test? (Report an to three decimal critical value = What is the test statistic for this sample? (Report answer accurate to three decimal places.) test statistic = The test statistic is... O in the critical region O not in the critical region This test statistic leads to a decision to... Oreject the null O accept the null O fail to reject the null As such, the final conclusion is that... O There is sufficient evidence to warrant rejection of the claim that the mean difference of post- test from pre-test is less than 0. There is not sufficient evidence to warrant rejection of the claim that the mean difference of post-test from pre-test is less than 0. O The sample data support the claim that the mean difference of post-test from pre-test is less than 0.
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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