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1 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 1 PREVIOUSLY used confidence intervals to answer questions such as... You know that 0.25% of women have red/green color blindness. You conduct a study of men and find that of 80 men tested, 7 have red/green color blindness. Based on the above information, do men have a higher percentage of red/green color blindness than women? Why or why not? question is whether a sample proportion differs from a population proportion

2 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 2 You measure body mass index (BMI) for 25 men and 25 women and calculate the following statistics... gender mean standard deviation women 25 6 men 26 3 The upper-limit for a NORMAL BMI for adults is 23. Based on the above information, can you say that either the group of women or group of men have 'above normal' BMIs? Why or why not? question is whether sample means differ from a population mean

3 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 3 You conduct health exams on samples of 25 men and 25 women. One measurement you make is systolic blood pressure (SBP) and you calculate the following statistics... gender mean standard deviation women men Do you think that men have a higher mean SBP than women? Why or why not? question is whether sample means differ from each other

4 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 4 ANOTHER APPROACH TO SUCH QUESTIONS hypothesis testing (almost always results in the same answer as confidence intervals - exception possible with proportions due to difference in how standard errors are calculated in hypothesis testing versus confidence intervals) one sample hypothesis testing does a sample value differ from a population value two sample hypothesis testing do two sample values differ from each other

5 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 5 DEFINITIONS Triola - hypothesis is a claim or statement about a property of a population Daniel - hypothesis is a statement about one or more populations Rosner - no explicit definition, but...preconceived ideas as to what population parameters might be...

6 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 6 Triola - hypothesis test is a standard procedure for testing a claim about a property of a population Daniel - no definition Rosner - no explicit definition, but... hypothesis testing provides an objective framework for making decisions using probabilistic methods rather than relying on subjective impressions...

7 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 7 COMPONENTS OF HYPOTHESIS TEST use a problem from the midterm... You measure body mass index (BMI) for 25 men and 25 women and calculate the following statistics... gender mean standard deviation women 25 6 men 26 3 The upper-limit for a NORMAL BMI for adults is 23. Based on the above information, can you say that either the group of women or group of men have 'above normal' BMIs? Why or why not? start with women

8 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 8 given a claim... women have above normal BMIs (above 23) identify the null hypothesis... the BMI of women is equal to 23 identify the alternative hypothesis... the BMI of women is greater than 23 express the null and alternative hypothesis is symbolic form... H 0 : µ = 23 H 1 : µ > 23

9 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 9 given a claim and sample data, calculate the value of the test statistic... t = ( X µ )/( S / N) = ( 25 23)/( 6 / 25) = 167. given a significance level, identify the critical value(s)... t-distribution with 24 degrees of freedom, α =.05, 1-tail test from table A-3 in Triola, critical value = 1.711

10 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 10 given a value of the test statistic, identify the P-value... in the t-distribution, what is the probability of obtaining the value of the test statistic (1.67) with 24 degrees of freedom interpolate in table A-3 in Triola, look on the line with 24 degrees of freedom and see that 1.67 lies between 0.05 and 0.10 for the area in one-tail, between the values ( α = 005. ) ( α = 010. ) or use STATCRUNCH to get an exact value... P = 0.054

11 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 11 state the conclusion of the hypothesis is simple, non-technical terms... there is no evidence to conclude that the BMI of women is not equal to 23 identify the type I and type II errors that can be made when testing a given claim... type I error determined by choice of α (in this case, 0.05) type II error is a function of... sample size variability (standard deviation) difference worth detecting size of chosen level of type I error

12 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 12 calculate type II error (formulas, SAS, web, etc.) from SAS... One-sample t Test for Mean Number of Sides 1 Null Mean 23 Alpha 0.05 Mean 25 Standard Deviation 6 Total Sample Size 25 Computed Power approximately a 50% chance of detecting a difference of 2 given the sample size, variability, and type I error

13 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 13 same methodology for men... H 0 : µ = 23 H 1 : µ > 23 t = ( X µ )/( S / N) = ( 26 23)/( 3 / 25) = 5 from table A-3 in Triola, critical value = P-value = E-5 (from STATCRUNCH) we reject the null hypothesis that BMI for men is equal to 23 and conclude that the BMI of men is greater than 23 power (from SAS) = > why is power so much different?

14 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 14 Mendel's genetics experiment H 0 : p = 0.25 H 1 : p 0.25 z = ( p\$ p)/ pq/ n = ( )/( ( 025. )( 075. )/ 580) = 067. from table A-2 in Triola, critical value = 1.96 P-value = (from table A-2 in Triola)

15 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 15 there is no evidence to conclude that the proportion of yellow pods is different from 0.25 power (from SAS) = 0.106) why is the power so low? to detect a difference of with power = 0.80, n=10,315

16 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 16 null and alternative hypotheses the null hypothesis ALWAYS contains a statement that the value of a population parameter is EQUAL to some claimed value the alternative hypothesis can contain a statement that a the value of a population is either NOT EQUAL to, LESS than, or GREATER than some claimed value (two-tailed and one-tailed hypothesis tests) implication of above on your own claim... your claim might be the null or it might be the alternative hypothesis "Gender Choice" increases the probability of a female infant, or... P(female) > 0.50 H 0 : p = 0.50 H 1 : p > 0.50

17 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 17 test statistic proportion z = ( p\$ p)/ pq/ n mean z = ( x µ )/( σ / n) or... t = ( x µ )/( s/ n) standard χ 2 = ( n 1) 2 / σ 2 deviation

18 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 18 critical values any value that separates the critical region (where the null hypothesis is rejected) from values that do not lead to rejection of the null hypothesis depends on alternative hypothesis and selected level of type I error not equal - two-tail test, 50% of area in each tail of the distribution greater or less than - one-tail test, 100% of area in one tail of the distribution two-tail test, test statistic is z, α = 005., critical value ±1.96 one-tail test, test statistic is z, α = 005., critical value ±1.645

19 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 19 P-value the probability of obtaining a value of the test statistic that is at least as extreme as the one calculated using sample data given that the null hypothesis is true calculation of an exact P-value depends on the alternative hypothesis (figure 7.6 in Triola) not equal - two-tail test, double value found in appropriate table greater or less than - one-tail test, value found in appropriate table

20 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 20 decisions and conclusions Traditional Method reject H 0 if the test statistic falls within the critical region, fail to reject H 0 if the test statistic does not fall within the critical region P-Value Method reject H 0 if P-value α fail to reject H 0 if P-value > α (or report the P-value and leave the decision to the reader)

21 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 21 Confidence Interval if the confidence interval contains the likely value of the population parameter, reject the claim that the population parameter has a value that is not included in the confidence interval Rosner - if testing H 0 : µ = µ 0 versus the alternative hypothesis H 1 : µ µ 0, H 0 is rejected if and only if a two-sided confidence interval for µ does not contain µ 0 Rosner - significance levels and confidence limits provide complimentary information and both should be reported where possible

22 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 22 TYPE I AND TYPE II ERRORS type I error - reject the null when it is actually true ( α ) type II error - fail to reject the null when it is actually false ( ) β power = 1 - (the probability of rejecting a false H 0 ) β

23 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 23 a look at power...

24 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 24 how does this diagram relate to the question about women's BMI... the UPPER DIAGRAM --- given a hypothesized mean of 23, a standard deviation of 6, a sample size of 25, and a one-tail test with alpha=0.05, any value greater than would be called "significantly greater" than 23 (would fall in the critical region)... ( )/( 6 / 25) = consider ~ 25, therefore, you would not reject the null hypothesis given any sample mean less than 25 the LOWER DIAGRAM --- if "reality" was a mean of 25, what portion of the area of a standard normal distribution would lie to the right of 25 (or ), answer is 50%, so the power ~ 0.50

25 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 25 another look at the same problem...

26 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 26 RELATIONSHIPS type I error (") increase - increase power (decrease type II error, \$) sample size (n) increase - increase power difference worth detecting ( µ µ 0 ) increase - increase power variability (F) increase - decrease power ", \$, n are all related fix any two, the third is determined

27 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 27 formula from Rosner - given a two-sided test, sample size for a given " and \$ n= σ ( z + z ) /( µ µ ) 1 β 1 α/ problem 7.9 from Rosner... plasma glucose level among sedentary people (check for diabetes)... is their level higher or lower than the general population among year olds, µ = 4.86 (mg/dl), F = 0.54 if a difference of 0.10 is worth detecting, with "=0.05, what sample size is needed to have 80% power (\$=0.20) 2 n= 054. ( z z0975. ) /( 010. ) 2 n = ( ) = ~

28 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 28 from SAS... Distribution Normal Method Exact Number of Sides 2 Null Mean 4.96 Alpha 0.05 Mean 4.86 Standard Deviation 0.54 Nominal Power 0.8 Computed N Total Actual N Power Total

29 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 29 from web site...

30 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 30 same data, different problem... problem 7.9 from Rosner... plasma glucose level among sedentary people (check for diabetes)... is their level higher or lower than the general population among year olds, µ = 4.86 (mg/dl), F = 0.54 if a difference of 0.10 is worth detecting, with "=0.05, with a sample size of 100, what is the power

31 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 31 same approach as the BMI problem... given a hypothesized mean of 4.86, a standard deviation of 0.54, a sample size of 100, and a two-tail test with alpha=0.05, any value greater than (or less than 4.754) would be called "significantly different" from 4.86 (would fall in the critical region)... ( )/( 054. / 100) = 196. ( )/( 054. / 100) = 196. if "reality" was a mean of 4.96, what portion of the area of a standard normal distribution would lie to the right of 4.966, answer is ~46%, so the power ~0.46

32 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 32 another look at the same problem illustration...shaded area on the right is power...

33 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 33 another look at the same problem illustration...shaded area on the left is power...

34 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 34 WHAT SHOULD YOU KNOW NOT formulas!!! what is power (from Rosner... the probability of detecting a significant difference) how to illustrate the concept of power (two normal curves drawn using information about the null hypothesis, ", sample size, F, difference worth detecting) what are Type I and Type II error the relationships among ", \$, n, F, µ µ 0

35 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 35 PROPORTION all the previous comments about the importance of proper sampling (random, representative) all conditions for a binomial distribution are met (fixed n of independent trials with constant p and only two possible outcomes for each trial) np\$5, nq\$5

36 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 36 test statistics is ALWAYS z... z = p\$ p/ pq/ n example from Rosner study guide... An area of current interest in cancer epidemiology is the possible role of oral contraceptives (OC s) in the development of breast cancer. Suppose that in a group of 1000 premenopausal women ages who are current users of OC s, 15 subsequently develop breast cancer over the next 5 years. If the expected 5- year incidence rate of breast cancer in this group is 1.2% based on national incidence rates, then test the hypothesis that there is an association between current OC use and the subsequent development of breast cancer.

37 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 37 claim... there is an association between OC use and subsequent development of breast cancer null and alternative hypothesis... H0: p = H : p calculate the test statistic... z = ( p\$ p)/ pq/ n = ( )/ ( )( )/ 1000 z = / = 087.

38 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 38 given a significance level, identify the critical value... "=0.05, z=1.96 (two-tail test) identify the P-value... from table A-2 what is area in tails of normal curve with z=0.87 (0.1992) from figure 7.6, two-sided test, double the P-value P=0.38 conclusion... no evidence to reject the null hypothesis

39 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 39 what is the confidence interval... CI = µ ± z( pq / n) = ± 196. ( )( )/ 1000 CI = ± (0.011, 0.019) same conclusion since is within the confidence interval

40 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 40 Triola problem given... n=1000 k=119 p=0.10 H : p = H : p traditional hypothesis test and P-value results not the same as the confidence interval results... why

41 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 41 can also calculate an exact P-value using the binomial distribution... this is the equivalent of a hypothesis test with an alternative hypothesis of... H : p > 010., not 1 H : p therefore... P-value=2(P)=0.0556

42 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 42 MEAN: F KNOWN all the previous comments about the importance of proper sampling (random, representative) the value of the population standard deviation is known the population is normally distributed or n > 30 (Central Limit Theorem)

43 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 43 test statistic WITH F KNOWN is z... z = ( x µ )/( σ / n) example from Rosner... We want to compare the serum cholesterol levels among recent Asian immigrants to the United States to that of the general US population. The mean of serum cholesterol in the US population is known to be 190 with a standard deviation of 40. We have serum cholesterol results on a sample of 100 recent immigrants and the mean value is

44 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 44 claim... the serum cholesterol of recent Asian immigrants differs from that in the US population null and alternative hypotheses... H H 0 1 : µ = : µ calculate the test statistic... z = ( )/( 40 / 100) = / 4 = 2. 12

45 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 45 given a significance level, identify the critical value... "=0.05, z=1.96 (two-tail test) identify the P-value... from table A-2 what is area in tails of normal curve with z=2.12 (0.0170) from figure 7.6, two-sided test, double the P-value P=0.034 conclusion... reject the null hypothesis

46 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 46 what is the confidence interval... CI = x ± z( σ / n) = ± 196. ( 4) CI = ± (173.68, ) same conclusion since 190 is not within the confidence interval

47 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 47 MEAN: F UNKNOWN all the previous comments about the importance of proper sampling (random, representative) the value of the population standard deviation is unknown the population is normally distributed or n > 30 (Central Limit Theorem)

48 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 48 test statistic WITH F UNKNOWN... t = ( x µ )/( s/ n) example from Rosner study guide... As part of the same program, eight year-old females report an average daily intake of saturated fat of 11 g with standard deviation = 11 g while on a vegetarian diet. If the average daily intake of saturated fat among year-old females in the general population is 24 g, then, using a significance level of.01, test the hypothesis that the intake of saturated fat in this group is lower than that in the general population.

49 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 49 claim... the daily intake of saturated fat of a group of females on a vegetarian diet is lower than that among females in the general population null and alternative hypotheses... H H 0 1 : µ = : µ < calculate the test statistic... t = ( 11 24)/( 11/ 8) = 13/ 389. = 3. 34

50 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 50 given a significance level, identify the critical value... "=0.01, 7 DF, t=2.998 (one-tail test) identify the P-value... from table A-3 what is area in the tail of a t-distribution with 7 DF, t=3.34 it is between (t=2.998) and (t=3.499) from Statcrunch, P=.0062 from figure 7.6, P= conclusion... reject the null hypothesis

51 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 51 STANDARD DEVIATION (OR VARIANCE) all the previous comments about the importance of proper sampling (random, representative) the population is normally distributed (Central Limit theorem does not apply here)

52 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 52 test statistic... χ = ( n 1) s / σ example from Rosner study guide... In a sample of 15 analgesic drug abusers, the standard deviation of serum creatinine is found to be The standard deviation of serum creatinine in the general population is Compare the variance of serum creatinine among analgesic abusers versus the variance of serum creatinine in the general population.

53 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 53 claim...the variability of serum creatinine among analgesic drug users differs from that in the general population null and alternative hypotheses... H H χ χ 0 1 : σ = 040. : σ 040. calculate the test statistic = ( n 1) s / σ = ( 15 1)( ) / = 1656.

54 HYPOTHESIS TESTING (ONE SAMPLE) - CHAPTER 7 54 given a significance level, identify the critical values... "=0.05, χ χ 2 14, = , = 563. identify the P-value... from Statcrunch... P=0.28 from figure 7.6, P=0.56 conclusion... accept the null hypothesis

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