A statistically significant result occurs when the value of the test statistic falls in the rejection region. A result has practical significance when it is statistically significant and the result also is different enough from results expected under the null hypothesis to be important to the consumer of the results. By taking large enough sample sizes, almost any result can be made statistically significant due to the increased ability of the test to detect a false null hypothesis, but small differences from the conditions expressed by the null hypothesis may not be important, that is, they may not have practical significance. This explains the difference between statistical significance and practical significance. Is this true? Explain your answer.
Yes, it is quite true. If the sample size increases, the factor square-root(n) increases, which goes into the numerator of the expression of calculating the z or the t-statistic. Consequently, the value of the test statistic increases, ...
It explains the difference between statistical significance and practical significance. The solution was rated '5/5' by the student who posted the question.