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      01-22-2019, 05:39 PM   #121
jlhymb
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Quote:
Originally Posted by bradleyland View Post
Maybe this will help you (from Wikipedia):

In statistical hypothesis testing, a result has statistical significance when it is very unlikely to have occurred given the null hypothesis.
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“Statistical significance helps quantify whether a result is likely due to chance or to some factor of interest,” says Redman. When a finding is significant, it simply means you can feel confident that’s it real, not that you just got lucky (or unlucky) in choosing the sample.
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Lol, the wheels never stop spinning with you... but at least your using google now and you're getting closer. Try googling the calculation for t test statistic and you will see that the denominator of the equation is part of the calculation used for the margin of error (critical value x standard deviation). You will notice that the margin of error and the p-value are related, and either or can be used to inform the interpretation of a hypothesis test.

The p-value for a t test run under the null hypothesis that the m2c time = the m3 zcp time is > .9. In null hypothesis testing, it is generally accepted that < .05 is the rejection region. Since .9 > .05 we fail to reject the null hypothesis and conclude that there is strong evidence to suggest the times are equal.

If you read the next two sentences in the wikipedia page you quoted, you will see the same. It states that a result is statistically significant when p<a. In our test, p>a, so the 2ms result is not considered statistically significant. Also, if you read my comments to Robin, you'll see the same sentiment shared with your HBR quote.

So thanks(?) for proving my point lol.

Last edited by jlhymb; 01-22-2019 at 05:47 PM.. Reason: spelling
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