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Statistically Significant result and Practical Significance

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What is the difference between statistical significance and practical significance? Why is statistical significance not necessarily of practical important difference to a business decision? Provide an example of this.

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Suppose that you are the manager of a large, multinational sales team and you have decided to offer incentive bonuses to your sales force if they meet certain levels of sales (measured by the mean weekly dollar volume of sales per salesperson) in an effort to increase your company's revenues and profits. Obviously if the sales team is increasing their sales volume then more money IS coming into the company's coffers. If the historical mean weekly sales volume per salesperson has been $20,000 then you would design a sales incentive plan to increase this number. In order to see if your sales team has increased its mean weekly sales volume per salesperson you do a hypothesis test on the population mean weekly sales amount per salesperson. In this example your hypotheses would be these:
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Solution Summary

This post offers an explanation of the difference between a result that is statistically significant but might not have any practical significance.

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Statistic Problem

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.

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