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Tests of Significance Section 10.2

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Tests of Significance Used to assess the evidence in favor of some claim. Example: Test sweetener in cola to see if it loses its sweetness over time. Positive # means loss of sweetness. Results of ten tasters: 2.0, 0.4, 0.7, 2.0, -0.4, 2.2, -1.3, 1.2, 1.1, 2.3 X = 1.02. Is this really loss of sweetness or just chance variation? Suppose we know that σ = 1.

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Sweetness Loss

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At x = 1.02 the p-value is.0006.

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Steps in Test of Significance (HATS) 1.H- Hypotheses: State assumption, null hypothesis, H 0, State what we suspect, the alternative hypothesis, H a. 2.A- Assumptions: Check that a test can be conducted and what type of test should be used. 3.T- Test: Calculate Test Statistic (sample statistic used to estimate a population parameter) and see how likely it could happen by chance (P-value is probability). 4.S- Summarize: results with small p-values (<.05) rarely occur if null hypothesis were true – statistically significant.

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To Compute P-value Ex: To find P(x sample mean), Find standard deviation of sampling distribution. Draw a picture. Standardize x to find P(Z z), P-value.

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To compute the z Test Statistic z = (x - µ 0 ) / (σ/n) If H 0 : µ = µ 0 –H a : µ > µ 0 find P(Z z) –H a : µ < µ 0 find P(Z z) –H a : µ µ 0 find P(Z z) or P(Z z) Draw each situation.

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Example: Mean systolic blood pressure for males is reported to be 128 with a st. dev. of 15. A sample of 72 executives have a mean of 126.07. Is this evidence that the executives have a different mean blood pressure from the general public? H H 0 : µ = 128; H a : µ 128 Draw!!! AAssumptions? TTest Statistic Z and P-value SSummarize Results

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Executives Example Test Statistic and P-value Summary: 27% of the time a sample would differ from the population in this way. This is not good evidence that the executives differ from others.

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Significance Level Sometimes we have a predetermined value of p, written as α, alpha. If the p-value is as small as, or smaller than, α, then we say The data are statistically significant at level α. If Z z* then reject null hypothesis. Never accept null hypothesis. Reject or fail to reject.

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Example: Test with given α Concentration of active ingredient is reported to be.86 with a st. dev. of.0068. A sample of 3 measurements have a mean of.8404. Is there significant evidence at the 1% level that µ.86? H H 0 : µ =.86; H 0 : µ.86 Draw!!! AAssumptions? TTest Statistic Z (Use table C to compare to 99% z*) SSummarize Results

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Example Test Statistic z Summary: z was much larger than z* so we reject the hypothesis that the concentration is.86.

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Significance Tests Steps (HATS): 1.H: ID Population and parameter of interest. State null and alternative hypothesis. 2.A: Choose appropriate inference procedure. Verify assumptions for using procedure. 3.T: Carry out Inference Test: 1.Make a sketch! 2.Calculate test statistic. 3.Find P-value. 4.S: Interpret results and summarize in the context of the problem.

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AP Statistics Section 11.2 A Inference Toolbox for Significance Tests.

AP Statistics Section 11.2 A Inference Toolbox for Significance Tests.

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