Lars wants to know if warming up will help runners sprint faster. Twenty-four track and field athletes volunteered to participate in his study. He randomly assigns 12 athletes to warm-up for 10 minutes. All 24 participants sprint the same distance. He calculates the mean for each group and determines that the mean for the warm-up group was 7.8 seconds and the mean for the other group was 9.8 seconds. To test the difference of means, he re-randomized the data 54 times and the differences are plotted in the dot plot below. What can Lars conclude from her study?
A. The difference in the means is significant because a difference of 2 is very likely. B. The difference in the means is significant because a difference of 2 is not very likely. C. The difference in the means is not significant because a difference of 2 is not very likely. D. The difference in the means is not significant because a difference of 2 is very likely.
@jim_thompson5910 Can you look at this real quick?
I think it is A or C.
ok let me read this over one sec
Null Hypothesis: H0: mu1 - mu2 = 2 Alternate Hypothesis H1: mu1 - mu2 =/= 2 If we tested the null hypothesis, we would assume the null is true for the time being. After doing the test, if we find the difference of 2 to be very unlikely, then we reject the null. The more unlikely 2 is to show up, the more statistically significant the conclusion (basically the more certain you are of the decision)
So it would be C?
so if we're focusing on +2, we see that it shows up only 1 time out of 54 times 1/54 = 0.0185 which is roughly 1.85% this is less than the default 5% threshold, making it statistically significant
so to sum things up the conclusion of rejecting the null IS significant since 2 barely shows up the difference of 2 is not very likely
So B, The difference in the means is significant because a difference of 2 is not very likely.?
yes, making us reject the null in favor of the alternate
Ok, thank you!
@jim_thompson5910 omg lol you answered this ahah
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