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Hypothesis Testing

Hypothesis Testing. Example:. Test the performance of two lists in terms of response rates Sample (1,000) from the first list provides a response rate of 3.5% Sample (1,200) from the second list provides a response rate of 4.5%

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Hypothesis Testing

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  1. Hypothesis Testing

  2. Example: • Test the performance of two lists in terms of response rates • Sample (1,000) from the first list provides a response rate of 3.5% • Sample (1,200) from the second list provides a response rate of 4.5% • Do the two lists (population) really have a difference or is it an artifact of the sample?

  3. The Logic of Hypothesis Testing • When you want to make statements about a population, you usually draw samples • How generalizable is your sample-based finding? • Evidence has to be evaluated statistically before arriving at a conclusion regarding the hypothesis • Depends on whether information is generated from the sample with fewer or larger observations

  4. Problem Definition Steps in Hyp Testing Clearly state the null and alternate hypotheses. Choose the relevant test and the appropriate probability distribution Determine the degrees of freedom Determine the significance level Choose the critical value Compare test statistic and critical value Compute relevant test statistic Decide if one-or two-tailed test Does the test statistic fall in the critical region? No Do not reject null Yes Reject null

  5. Basic Concepts of Hypothesis Testing • The Null and Alternate hypothesis • Choosing the relevant statistical test and appropriate probability distribution. Depends on - Size of the sample - Whether the population standard deviation is known or not • Choosing the Critical Value. The three criteria used are - Significance Level - Degrees of Freedom - One or Two Tailed Test

  6. Significance Level • Indicates the percentage of sample means that is outside the cut-off limits (critical value) • The higher the significance level () used for testing a hypothesis, the higher the probability of rejecting a null hypothesis when it is true (Type I error) • Accepting a null hypothesis when it is false is called a Type II error and its probability is ()

  7. Significance Level (Contd.) • When choosing a level of significance, there is an inherent tradeoff between these two types of errors • Power of hypothesis test (1 - ) • A good test of hypothesis ought to reject a null hypothesis when it is false • 1 -  should be as high a value as possible

  8. Degree of Freedom • The number or bits of "free" or unconstrained data used in calculating a sample statistic or test statistic • A sample mean (X) has `n' degree of freedom • A sample variance (s2) has (n-1) degrees of freedom

  9. One or Two-tail Test • One-tailed Hypothesis Test • Determines whether a particular population parameter is larger or smaller than some predefined value • Uses one critical value of test statistic • Two-tailed Hypothesis Test • Determines the likelihood that a population parameter is within certain upper and lower bounds • May use one or two critical values

  10. Hypothesis Testing

  11. Hypothesis Testing About a Single Mean - Step-by-Step 1) Formulate Hypotheses 2) Select appropriate formula 3) Select significance level 4) Calculate z or t statistic 5) Calculate degrees of freedom (for t-test) 6) Obtain critical value from table 7) Make decision regarding the Null-hypothesis

  12. Hypothesis Testing About a Single Mean - Example 1(2 tailed) • Ho:  = 5000 (hypothesized value of population) • Ha:   5000 (alternative hypothesis) • n = 100 = 4960 •  = 250 •  = 0.05 Rejection rule: if |zcalc| > z/2 then reject Ho.

  13. Hypothesis Testing About a Single Mean - Example 2 • Ho:  = 1000 (hypothesized value of population) • Ha:   1000 (alternative hypothesis) • n = 12 = 1087.1 • s = 191.6 •  = 0.01 Rejection rule: if |tcalc| > tdf, /2 then reject Ho.

  14. Hypothesis Testing About a Single Mean - Example 3(1 tailed) • Ho:  5000 (hypothesized value of population) • Ha:  < 5000 (alternative hypothesis) • n = 50 = 4970 •  = 250 •  = 0.01 Rejection rule: if then reject Ho.

  15. Hypothesis Test of Difference between Means • Mayor of a city wants to see if males and females earn the same • A random sample of 400 males and 576 females was taken and following was found

  16. Hypothesis Test of Difference between Means • The appropriate test depends on - whether samples are from related or unrelated samples - whether population standard deviations are known or not - if not, whether they can be assumed to be equal or not

  17. Hypothesis Test of Difference between Means • In salary example, the null hypothesis is Ho: 1- 2 =c (=0) Ha: 1- 2 c • Since we have unrelated samples with known (for large samples, we can use sample SD as pop SD) but unequal ’s the standard error of difference in means is

  18. Hypothesis Test of Difference between Means • The calculated value of z is • For =.01 and a two-tailed test, the Z-table value is 2.58 • Since is greater than , the null hypothesis is rejected

  19. Hypothesis Testing of Proportion • Quality control dept of a light bulb company claims 95% of its products are defect free • The CEO checks 225 bulbs and finds only 87% to be defect free • Is the claim of 95% true at .05 level of significance ? • So we have hypothesized values and sample values

  20. Hypothesis Testing of Proportion • The null hypothesis is Ho:p=0.95 • The alternate hypothesis is Ha: p 0.95 • First, calculate the standard error of the proportion using hypothesized values as • Since np and nq are large, we can use the Z table. The appropriate z value is 1.96

  21. Hypothesis Testing of Proportion • The limits of the acceptance region are • Since the sample proportion of 0.87 does not fall within the acceptance region, the CEO should reject the quality control department’s claim

  22. Hypothesis Testing of Difference between Proportions • Manager wants to see if John and Linda, two salespeople, have the same conversion • He picks samples and finds that

  23. Hypothesis Testing of Difference between Proportions • Are their conversion rates different at 0.05 significance level? • The null hypothesis is Ho: • The alternate hypothesis is Ha: • The best estimate of p (proportion of success) is also,

  24. Hypothesis Testing of Difference between Proportions • An estimate of the standard error of the difference of proportions is • The z value can be calculated as • The z value obtained from the table is 1.96 (for ). Thus, we fail to reject the null hypothesis

  25. The Probability Values (P-value) Approach to Hypothesis Testing • P-value provides researcher with alternative method of testing hypothesis without pre-specifying  • Largest level of significance at which we would not reject Ho

  26. The Probability Values (P-value) Approach to Hypothesis Testing Difference Between Using  and p-value • Hypothesis testing with a pre-specified  • Researcher is trying to determine, "is the probability of what has been observed less than ?" • Reject or fail to reject Ho accordingly

  27. The Probability Values (P-value) Approach to Hypothesis Testing Using the p-Value • Researcher can determine "how unlikely is the result that has been observed?" • Decide whether to reject or fail to reject Ho without being bound by a pre-specified significance level • In general, the smaller the p-value, the greater is the researcher's confidence in sample findings

  28. The Probability Values (P-value) Approach to Hypothesis Testing: Example • Ho:  = 25 (hypothesized value of population) • Ha:   25 (alternative hypothesis) • n = 50 = 25.2 •  = 0.7 • SE( )= = 0.1; Z= =2 • From Z-table, prob Z >2 is 0.0228. As this is a 2-tailed test, the p-value is 2 0.228=.0456

  29. The Probability Values (P-value) Approach to Hypothesis Testing Using the p-Value • P-value is generally sensitive to sample size • A large sample should yield a low p-value • P-value can report the impact of the sample size on the reliability of the results

  30. Relationship between C.I and Hypothesis Testing (Example 1) • A direct mktr knows that average no of purchases per month in entire database is 5.6 • By sampling ‘loyals’ he finds that their average is 6.1(i.e, =6.1) • Is it merely a sampling accident? • Ho:  = 5.6 (hypothesized value of population) • Ha:   5.6 (alternative hypothesis) • n = 35 •  = 2.5

  31. Relationship between C.I and Hypothesis Testing (Example 1) • Std err =0.42 • The appropriate Z for =.05 is 1.96 • The Confidence Interval is = (4.78, 6.42) • Since 6.1 falls in the interval, we cannot reject the null hypothesis

  32. Confidence Intervals and Hypothesis Testing t = = Interval estimate for • Hypothesis testing and Confidence Intervals are two sides of the same coin.

  33. Relationship between C.I and Hypothesis Testing (Example 2) • Revisit the first example we started with • Test the performance of two lists in terms of response rates • Sample (1,000) from the first list provides a response rate of 3.5% • Sample (1,200) from the second list provides a response rate of 4.5% • Do the two lists (population) really have a difference or is it an artifact of the sample?

  34. Relationship between C.I and Hypothesis Testing (Example 2) • C.I. of list 1: • (0.035)+/- 1.96*(SE1) • SE1 = Sqrt[(0.035*0.965)/1000]=0.006 • C.I.1=(0.0232,0.0467) • C.I. of list 2: • (0.045)+/-1.96*(SE2) • SE2=Sqrt[(0.045*0.955)/1200]=0.006 • C.I.2 =(0.033,0.0568) • What can we infer based on these confidence Intervals? • Lack of sufficient evidence to infer that there is any difference between the response rates in the two samples.

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