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Introductory Statistics for Business BBA 2018/19. Testing Hypotheses. Partially based on Business Statistics, N. Sharpe et al. Example One common theory of market behavio u r says that on a given day, the market is just as likely to move up as down.
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IntroductoryStatistics for BusinessBBA 2018/19 TestingHypotheses Partially based on Business Statistics, N. Sharpe et al.
Example One common theory of market behaviour says that on a given day, the market is just as likely to move up as down. Let’s investigate if WIG20 is just as likely to move higher or lower on any given day. Out of the 181 trading days in that period, the closing price increased on 98 days - 54.44%. That is more “up” days than “down” days, but is it far enough from 50% to cast doubt on the assumption of equally likely up or down movement?
Hypotheses To test whether the daily fluctuations are equally likely to be up asdown, we assume that they are, and that any apparent difference from 50% is just random fluctuation. So, our starting hypothesis, called the null hypothesis, is that the proportion of days on which the WIG20 increases is 50%.
Hypotheses - WIG20 example QUESTION: What would convince us that the proportion of „up” days was not 50%? How different from 50% must the sample proportion be before we would be convinced that the true proportion wasn’t 50%?
P-Values The fundamental step in our reasoning is the question: “Are the data surprising, given the null hypothesis?” And the key calculation is to determine exactly how likely the data we observed would be if the null hypothesis were the true model.We want to find the probability of seeing data like these (or something even less likely) given the null hypothesis. The P-valueis the probabilitythat the data would take values as extreme or more extreme than that actually observedgiven the nullhypothesis.
P-value There is no hard and fast rule about how low the P-value has to be. Almost everyone would agree, however, that a P-value less than 0.001 indicates very strong evidence against the null hypothesis but a P-value greater than 0.1 provides very weak evidence. A low enough P-value says that the data we have observed would be very unlikely if our null hypothesis were true.So we should rejectthe null hypothesis. When the P-value is high (or just not low enough), we have no reason to reject the null hypothesis. Formally, we say that we “fail to reject” the null hypothesis.
Hypotheses Example (Sharpe et al.) Summit Projects is a full-service interactive agency, that offerscompanies a variety of website services.One of Summit’s clients is SmartWool, which produces and sells wool apparel. Summitrecently re-designed SmartWool’s apparel website, and analysts at SmartWool wondered whether traffic has changed since thenew website went live. In particular, an analyst might want to know if the proportion of visits resulting in a sale has changed sincethe new site went online. She might also wonder if the average sale amount has changed. Questions: If the old site’s proportion was 20%, frame appropriate null and alternative hypotheses for the proportion. If lastyear’s average sale was $24.85, frame appropriate null and alternative hypotheses for the mean.
Hypotheses - SmartWoolexample Answer: The proportion of visits that resulted in a sale since the new website went online is very unlikely to still be 0.20. Thereis strong evidence to suggest that the proportion has changed. She should reject the null hypothesis. However, the mean amountspent is consistent with the null hypothesis and therefore she is unable to reject the null hypothesis that the mean is still $24.85against the alternative that it increased.
Alternative Hypotheses the rejection region
Alternative Hypotheses An alternative hypothesis that focuses on deviations from the null hypothesis value in only one direction is called a one-sided alternative. For a hypothesis test with a one-sided alternative, the P-value is the probabilityof deviating only in the direction of the alternative away from the null hypothesis value.
Testinghypothesisaboutproportion Example (Sharpe et al.) Anyone who follows or plays sports has heard of the “home field advantage.” It is said that teams are more likely to win when they play at home. But is it true? In the 2009 Major League Baseball (MLB) season, there were 2430 regular season games. It turns outthat the home team won 1332 of the 2430 games, or 54.81% of the time. If there were no home field advantage, the home teams would win about half of all games played. Could this deviation from 50% be explained just from natural sampling variability, or does this evidence suggest that there really is a home field advantage, at least in professional baseball? To test the hypothesis, we will ask whether the observed rate of home team victories, 54.81%, is so much greater than 50% that we cannot explain it away as just chance variation.
Testinghypothesisaboutproportion – home field advantage The one-proportion z-test: z
Conclusion: Our analysis of outcomes during the 2009 Major League Baseball season showed a statistically significant advantage to the home team. We can be quite confident that playing at home gives a baseball team an advantage.
Testing hypotheses about means Example (Sharpe et al.) Insurance company. A manager wanted to see how well one of her sales representatives was doing, so she selected 30 matured policies that had been sold by the sales rep and computed the (net) profit (premium charged minus paid claims), for each of the 30 policies. From these 30 policies, the management would like to know if there’s evidence that the mean profit of policies sold by this sales rep is less than $1500. -
Alpha Levels and Significance How low does the P-value have to be to rejectnullhypothesis? You must select the alpha level beforeyou look at the data. Consideryour alpha levelcarefully and choose an appropriate one for the situation. When we reject the null hypothesis, we say that the test is “significant at that level.”
TwoTypes of Errors TypeI. The null hypothesis is true, but we mistakenly reject it. TypeII. The null hypothesis is false, but we fail to reject it.
