Hypothesis Testing. Hypothesis Testing


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1 Hypothesis Testing Daniel A. Menascé Department of Computer Science George Mason University 1 Hypothesis Testing Purpose: make inferences about a population parameter by analyzing differences between observed sample statistics and the results one expects to obtain if some underlying assumption is true. Null hypothesis: H 0 : µ = x Alternative hypothesis: H1 : µ x If the null hypothesis is rejected then the alternative hypothesis is accepted. 2 1
2 0.0 µ rejection nonrejection rejection critical values Test statistic: Z X µ = σ n 3 Risks in Decision Making Type I Error occurs if H o is rejected when it is true. Pr [H o is rejected true] = α Type II Error occurs if H o is not rejected when it is false. Pr[H o is not rejected false] = β Confidence coefficient: Pr [H o not rejected true]= 1 α Power of the test: Pr[H o is rejected false]= 1β 4 2
3 Actual Situation Accept H o H o true Correct decision Confidence=1α H o false Type II Error: Pr[Type II]=β Reject H o Type I Error P[Type I]=α Correct Decision Power=1β 5 Example of Hypothesis Testing A sample of 50 files from a file system is selected. The sample mean is 12.3Kbytes. The standard deviation is known to be 0.5 Kbytes. Ho: µ = 12.5 Κbytes H 1 : µ 12.5 Κbytes Confidence:
4 rejection=0.025 rejection= nonrejection = 0.95 NORMINV(10.05/2,0,1) Z = = Reject H o 7 Z Test of Hypothesis for the Mean Null Hypothesis m= 12.5 Level of Significance 0.05 Population Standard Deviation 0.5 Sample Size 50 Sample Mean 12.3 Standard Error of the Mean Z Test Statistic TwoTailed Test Lower Critical Value Upper Critical Value pvalue Reject the null hypothesis 8 4
5 Steps in Hypothesis Testing 1. State the null and alternative hypothesis. 2. Choose the level of significance α. 3. Choose the sample size n. Larger samples allow us to detect even small differences between sample statistics and true population parameters. For a given α, increasing n decreases β. 4. Choose the appropriate statistical technique and test statistic to use (Z or t). 9 Steps in Hypothesis Testing 5. Determine the critical values that divide the regions of acceptance and nonacceptance. 6. Collect the data and compute the sample mean and the appropriate test statistic (e.g., Z). 7. If the test statistic falls in the nonreject region, H o cannot be rejected. Else H o is rejected. 10 5
6 The pvalue Approach pvalue: observed level of significance. Defined as the probability that the test statistic is equal to or more extreme than the result obtained from the sample data, given that H o is true. 11 p/2 p/2 0 Z Z p/2=f(z) =NORMDIST(z,0,1,true) critical values If p α then do not reject H o, else reject H o. 12 6
7 p/2 p/2 0 Z Z p/2=f(z) =NORMDIST(z,0,1,true) critical values Do not reject H o 13 p/2 p/2 0 Z Z p/2=f(z) =NORMDIST(z,0,1,true) critical values Reject H o 14 7
8 NORMDIST( ,0,1,TRUE) Computing pvalues Z Test of Hypothesis for the Mean Null Hypothesis m= 12.5 Level of Significance 0.05 Population Standard Deviation 0.5 Sample Size 50 Sample Mean 12.3 Standard Error of the Mean Z Test Statistic TwoTailed Test Lower Critical Value Upper Critical Value pvalue Reject the null hypothesis The null hypothesis is rejected because p (0.0047) is less than the level of significance (0.05). 15 Steps in Determining the pvalue. 1. State the null and alternative hypothesis. 2. Choose the level of significance α. 3. Choose the sample size n. Larger samples allow us to detect even small differences between sample statistics and true population parameters. For a given α, increasing n decreases β. 4. Choose the appropriate statistical technique and test statistic to use (Z or t). 16 8
9 Steps in Determining the pvalue. 5. Collect the data and compute the sample mean and the appropriate test statistic (e.g., Z). 6. Calculate the pvalue based on the test statistic 7. Compare the pvalue to α. 8. If p α then do not reject H o, else reject H o. 17 Onetailed Tests Null hypothesis is an inequality. H o 3.5 H 1 <
10 α 1α 0.0 µ rejection nonrejection critical value Test statistic: Z X µ = σ n 19 Example of OneTailed Test A sample of 50 files from a file system is selected. The sample mean is 12.35Kbytes. The standard deviation is known to be 0.5 Kbytes. Ho: µ 12.3 Κbytes H 1 : µ < 12.3 Κbytes Confidence:
11 Example of OneTailed Test Z X µ = σ / n = = / 50 (test statistic) Critical value =NORMINV(0.05,0,1)= Region of nonrejection: Z So, do not reject H o. (Z exceeds critical value) (α) 0.95 (1 α) 0.0 µ rejection nonrejection (test statistic) (critical value) Test statistic: Z X µ = σ n 22 11
12 Onetailed Test Z Test of Hypothesis for the Mean Null Hypothesis m= 12.3 Level of Significance 0.05 Population Standard Deviation 0.5 Sample Size 50 Sample Mean Standard Error of the Mean Z Test Statistic LowerTail Test Lower Critical Value pvalue Do not reject the null hypothesis 23 Hypothesis Tests with Unknown σ If the population is assumed to be normally distributed the sampling distribution for the mean follows a t distribution with n1 degrees of freedom. t statistic for unknown σ: t = X µ s n Use sample standard deviation 24 12
13 Example of Hypothesis Testing A sample of 50 files from a file system is selected. The sample mean is 12.3Kbytes. The sample standard deviation is 0.5 Kbytes. Ho: µ = Κbytes H 1 : µ Κbytes Confidence: t Test of Hypothesis for the Mean Null Hypothesis m= Level of Significance 0.05 Sample Size 50 Sample Mean 12.3 Sample Standard Deviation 0.5 Standard Error of the Mean Degrees of Freedom 49 t Test Statistic TINV(0.05,49) TwoTailed Test Lower Critical Value Upper Critical Value p Value Do not reject the null hypothesis The t test statistic is between the lower and critical values. So, do not reject the null hypothesis
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