Please explain as to how Options A & B are a valid explanation for divergence and Option C is not.
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Statistical significance refers to the use of a sample to carry out a statistical test meant to reveal any significant deviation from the stated null hypothesis. We then decide whether to reject or not to reject the null hypothesis.
Economic significance entails not just the statistical significance but also the economic effect inherent in the decision made after data analysis and testing. Think of it as economic or practical feasibility of the statistical result.
Some statistically significant results may not be economically feasible, owing to factors such as,
Transaction costs – imagine a statistically significant study about the stock markets which shows that one can earn a profit by following such buying and selling rules as mentioned in the study – but it may turn out that once you account for transaction costs (a real life, practical problem) the result of the statistical study might not be feasible.
Risk – imagine a similar statistically significant study about the stock markets. If in case the activities involve the trader to take upon large amounts of risks outside the ability and willingness of the trader, the result of the study may not be practically or economically feasible.
Option C is the correct answer. Data errors cannot account for the difference between statistical significance and economic significance. Data errors in the statistical study would lead to incorrect results in the first place. It could be a reason for an incorrect statistical result, which is why it does not account for a statistically significant study not being economically significant.
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