Practical v. Statistical Significance
The total length of the videos in this section is approximately 8 minutes.
Practical v. statistical significance
Question 1: For a particular study, we expect that larger sample size will lead to
bigger t-statistic and smaller p-value
smaller t-statistic and larger p-value
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bigger t-statistic and smaller p-value
Question 2: The sample size determines:
practical significance
statistical significance
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Statistical significance. The larger the sample size, the larger the t-statistic will be, and the more likely it will be to be statistically significant. The sample size has no bearing on practical significance. Try writing out the expression for a t-statistic, choosing some arbitrary numbers if you like. When you increase n, t gets bigger, which means that the p-value would get smaller, even if the numerator of the t-statistic stays the same.
Question 3: Check all of the statements that are true.
Irrelevant but non-zero population differences will be statistically significant, in large enough samples
Important population differences may not be statistically significant, but only because the sample is too small
p-value cut-offs for rejecting null hypothesis are arbitrary
p-value is the probability that the null hypothesis is true
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The first three, but not the fourth.
That's all.
During this tutorial you learned:
The definition of p-value
The difference between practical significance vs statistical significance
The relationship between a t-statistic and sample size
The cutoff threshold of p-values is arbitrary
Terms and concepts:
P-value, practical significance, statistical significance