# A POPULATION MEAN, CONFIDENCE INTERVALS AND HYPOTHESIS TESTING

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1 CHAPTER 5. A POPULATION MEAN, CONFIDENCE INTERVALS AND HYPOTHESIS TESTING 5.1 Concepts When a number of animals or plots are exposed to a certain treatment, we usually estimate the effect of the treatment by the mean response of the experimental units. Statistically this is referred to as a point estimate of the parameter from the population of all possible treated animals or plots. Different samples, however, yield different estimates. It is desirable, therefore, to have a range of values so that we have a high degree of confidence that the true treatment mean will fall within this range. This is called confidence interval estimation. These ideas can be illustrated by the following example. Suppose we wish to know the mean heart rate of female horses (mares) after being treated by a drug. A random sample of 25 treated mares is taken and the heart rate of each is determined. The average heart rate is 40.5 beats per minute, and the standard deviation of the 25 determinations is 3. With 95% confidence, we would like to know a range of heart beats within which the population mean will fall. From t he standard deviation of our sample we estimate the standard error, S y = 3/ 25 = 0.6, with df = 24. The 95% confidence interval is calculated as y ± t α S y = (0.6) = , That is between and Based on the sample, our best single value estimate of μ is the sample mean, Now, however, we are 95% confident that the μ is equal to or greater than and smaller than or equal to The idea of confidence intervals is related to the concept of hypothesis testing. A statistical hypothesis is an assumption made about some parameter of a population. The hypothesis being tested is often denoted by H 0 and is called the null hypothesis since it implies that there is no real difference between the parameter and its hypothesized value. For example, it may have been hypothesized that the mean heart rate of the treated mare population (μ) is the same as that of the nontreatment population which is known to be 39 beats/minute. The null hypothesis to be tested is that μ = 39. A statistical procedure which provides a probablistic assessment of the truth or falsity of a hypothesis is called a statistical test. Figure 5-1 illustrates the process of hypothesis testing. The confidence interval procedure illustrated above can also be used for the purpose of hypothesis testing. For this example, the 95% confidence interval does not bracket the hypothesized value of 39, and therefore the sample data do not support the null hypothesis.

2 Fig Procedure for testing a null hypothesis, H 0 : μ = μ*. The experiment is interested in a population with an unknown mean, μ. He wishes to test the hypothesis that the unknown mean is equal to a value μ*. The theoretical distribution of sample means drawn from the population centering on μ* is shown on the left. On the right the experimental information obtained from a sample is illustrated. The final stage of the testing process is to compare the experimental results with the hypothesized situation. An alterative hypothesis can be denoted by H 1 and will be accepted if H 0 is rejected. The nonrejection of H 0 is a decision not to accept H 1. Our alternative hypothesis for the mare heart rate study can be H 1 : μ 39. Since the null hypothesis, μ = 39, was rejected, we accept this alternative. Note that the nonrejection of H 0 is not always equivalent to accepting H 0. It merely says that from the available information, there is not sufficient evidence to reject H 0. It merely says that from the available information, there is not sufficient evidence to reject H 0. In fact, H 0 may be rejected if further evidence so indicates. However, in practice it is common to use the acceptance of H 0 interchangeably with the nonrejection of H 0.

3 In stating the hypothesis that the mean heart rate of mares is 39, H 0 : μ = 39, versus the alternative, H 1 : μ 39, we reject H 0 if the observed sample mean is significantly greater or smaller than 39. This is a two-tailed test. If, based on prior information, we know that the true mean heart rate cannot be less than 39, our alternative hypothesis would be, H 1 : μ > 39. In this case, the test is called a one-tailed test. That is, we will reject the null hypothesis only if the observed sample mean is significantly greater than 39. Figure 5-2 illustrates these situations. Figure 5-2. One-tailed or two-tailed test: the mare heart rate example at α = 5%. One-tailed test is a more directed test; the experimenter must know the direction of the difference if it occurs. Therefore, it is more sensitive (smaller critical value) in detecting the differences than a two-tailed test at the same α level. Whenever a decision is made to reject or not to reject a null hypothesis, there is always a possibility that the conclusion is incorrect. This point can be illustrated in the following figure by considering the distribution of heart rate means from a sample of 25 mares.

4 For this situation, if the true mean is 39, we will still have a 5% chance of obtaining a sample mean of or greater. Thus, if we reject H 0 for any value greater than 40.03, we have a 5% or less chance of making a wrong decision. This is called a type I error. If the true mean is and we do not reject H 0 for any value less than or equal to 40.03, we will have at least a 20% chance of being wrong. This is called a type II error. The probability of committing a type I error is commonly denoted as, the level of significance, and the probability of making a type II error is commonly called. The experimenter can usually set the level of significance, i.e., the risk one is willing to take of making a type I error -- the rejection of the proposed null hypothesis when it is true. Since the true mean is never known, it is not possible to determine, the probability for a type II error. The probability of detecting a false null hypothesis is called the power (1 - ) of the test. 5.2 Level of Significance The level of significance of a statistical test is the probability level, α, for obtaining a critical value under the distribution specified by the null hypothesis. A calculated test-statistic is compared to the critical value to make a decision as to rejection or nonrejection of the null hypothesis. The rejection of the null hypothesis is equivalent to saying that the calculated probability for obtaining the test statistic is less than α. Suppose we observe a band of zebras to determine the sex of newborn foals. Our null hypothesis may be that there is a chance for each foal to be a colt ( ) or a filly ( ). The alternative hypothesis is that the chance is not 50%. If the null hypothesis is true, the most likely ratio of : from the 10 newborn foals is 5.5, from Appendix Table A-2, a probability of 24.6%. Ten or zero males are the most unlikely occurrence as a 10:0 or 0:10 ratio is the furthest from 5:5. The chance of all 10 being males is =.001, or 0.1%, and the chance of all 10 being female is also 0.1%. Thus, the chance of 10 or zero males is 0.2%. If among 10 observed foals, 3 or less are males or 3 or less are females, the observed ratios are [0:10, 1:9, 2:8, 3:7] or [7:3, 8;2, 9:1, 10:0] and the probabilities of these ratios are 34.4%, which implies that it is not too uncommon to see these ratios even when the true ratio is 5:5. Therefore, a 3:7 or 7:3 ratio gives little basis for rejecting the null hypothesis. If, on the other hand, the observed ratio was 1:9 or 9:1, or more extreme ratios, the probability is 2.2%. This suggests that the chance of a newborn foal being male or female may not be truly 50%. Thus, we may wish to reject the null hypothesis and accept the alternative.

5 The probability at which the null hypothesis is rejected is called the level of significance. Thus, in the mare example, we rejected the null hypothesis of mean heart rate equal to 39 at the 5% significance level. If in testing the sex ratio of zebra foals, we being to reject the null hypothesis of a chance to produce either sex at an observed ratio of 9:1 or more extreme, our significance level is 2.2%. Thus, the probability of incorrectly rejecting the null hypothesis is 2.2%, the probability of committing a type I error. Figure 5-3 illustrates the use of the significance level in hypothesis testing. Biologists traditionally use a significance level of 5% or 1% for rejection. The difference between the observation and the hypothesized parameter that results in rejection at the 5% level of significance is commonly called a "significant difference." If the rejection is based on the 1% significance level, the difference is termed "highly significant". 5.3 A Normal Population Mean In this section we discuss the steps involved in determining confidence limits from a single sample about a population mean and the testing of a proposed hypothesis. For illustration, we will consider root yield data from a sugarbeet experiment conducted by Hills at U.C. Davis in This investigation involved six rates of nitrogen fertilizer from 0 to 250 lb/acre in 50 lb increments. For the present discussion we will use data (Table 5-1) from the three highest N rates as a sample of root yields from a population of heavily fertilized plots. Table 5-1. Sugarbeet root yields (tons/acre-fresh weight) from heavily fertilized plots

6 To construct the 95% confidence interval for the population mean we proceed as follows: 1. Compute the mean and the variance of the sample; Y = ΣY i /r =606.2/15 = ( Σ Y) S = { Σ Y i ]/( r 1 ) r = { (606.2) 2 /15}/14 = 0.91 with df = 15-1 = Compute the standard error:

7 S = ( 2 y S / r ) = 091. / 15 = Find the tabular t for 14 df and = 0.05 (two-tailed) from Appendix Table A-6: t.05,14 = Multiply t by S y ; t α S y = ( 0. 25) = Determine the confidence limits, L = lower limit and U = upper limit: L U Y t S = ± α y = ± 053. = Interpretation: We are 95% confident that the true mean root yield of heavily fertilized sugarbeet plots represented by this sample is between 39.9 and 40.9 tons per acre. Suppose that the experimenter expects the mean yield of well-fertilized plots to be greater than 39.8 tons per acre on the basis that the yield must be at least this amount to economically justify a high level of fertilization. This line of thinking generates the hypothesis to be evaluated. Assume that the experimenter is not willing to accept more than a 5% risk of making a type I error. 1. Set the null hypothesis, alternative hypothesis, and level: H 0 : < H 1 : > = 5% (one-tailed test) 2. Calculate the mean and the standard error for the sample: Y = S y = 025. with df = 15-1 = 14

8 3. Calculate a t-value: t = Y μ = = S y 4. Compare the calculated t with the tabular t for = 5% (one-tailed test) and df = 14. Since our calculated t is greater than the tabular critical t α value (1.76), we reject the H 0 and accept the H 1. Based on the information provided by the sample, this leads to the conclusion that the true mean yield of heavily fertilized plots is significantly greater than 39.8 tons per acre. The probability that the true mean yields is less than or equal to 39.8 tons per acre is less than 5%. With sample sizes larger than 30, t closely approaches Z, the standard normal deviate. Therefore, the tabular Z values can be used in place of tabular T values for the above computations. For determining confidence limits: L U = Y ± Z S α y For hypothesis testing: Z = ( Y μ ) / S y The calculated Z is compared with tabular Z values to draw conclusions. 5.4 A Proportion From a Binomial Population Recall the discussion in Section 4.3 concerning the approximation of a binomial distribution from the normal distribution. In that section we showed that one use of the Z distribution is to determine probabilities for binomial events. Now suppose we are interested in determining the percent of underweight cans resulting from a special canning process. We draw a random sample of 100 (n) cans and find that 14 (r) of them are underweight. What is the 95% confidence interval that includes the true percentage of underweight cans? 1. Compute the mean and the variance of the proportion of underweight cans in the sample: \$ 14 P = = 014. = YP P\$ ( 1 P\$ ) 014. ( 086. ) 012. S \$ = = = = P n Compute the standard error: S = P\$ ( 1 P\$ )/ n = 012. / 100 = P \$ 3. Find the tabular Z α for α = (1-0.95) = 0.05;

9 Note that in order to find the two-tailed Z 0.05 value from the cumulative normal table, we actually have to find the one-tailed Z value, Z = Multiple Z by; Z α \$ = 196. ( ) = S P 5. Determine the confidence limits L and U: L U = P \$ ± Z S α P\$ 007. = 014. ± 007. = 021. With a confidence of 95%, we can say that the true proportion of underweight cans is between 7% and 21%. Similarly, we can test a hypothesis regarding the true proportion of underweight cans by: Z = P\$ P P( 1 P)/ n where P is the hypothesized proportion. Note that since P is available from the null hypothesis, it is used to calculate the standard error and is denoted as σ p. Whether the test will be one or two tailed depends on the statement of the alternative hypothesis. For example: H 0 : P = 10% H 1 : P 10% These statements indicate a two-tailed test because H 1 states P < 10% or P > 10%. For a test at the 5% significance level, the probability for each tail should be 2.5%. If H 0 : P = 10%

10 H 1 : P > 10% These statements indicate a one-tailed test because the H 1 states the direction of the difference if P is not equal to 10%. Therefore, the Z α value to be expected is 1.645, which excludes 5% of the area under the normal curve. In our example: Z = P\$. 10 (. 10)(. 90)/ = = which is less than the tabular Z-value, Thus, there is no evidence to reject the null hypothesis. 5.5 Sample Size Determination It is often necessary to determine the sample size required to estimate a population mean within an acceptable limit of error. An approximate solution can be obtained by the use of the Z formula: Z = Y μ σ y Note that σ σ α σ y = / r, and thus r = Z / d where d = ( Y μ ), a measure of the difference between the estimated mean and the population mean. The experimenter can specify the magnitude of the difference that he wishes to detect. To use this formula, one needs to know the population standard deviation. Since this is seldom known, an estimate is substituted to obtain an approximate solution. Suppose we want to determine with a confidence of 95%, the sample size (the number of plots), the mean of which will be within one ton of the population mean. From previous experience, the standard deviation for root yield of plots of the size and shape we wish to use, is estimated as 1.4 tons per acre. From the conditions given, the sample size is determined by the following steps: 1. d = 1 ton per acre and = 1.4 tons per acre. 2. Since the deviation of the sample mean from the population mean can be ± 1 (d = 1), the 95% confidence implies a two-tailed probability equal to 5%. Thus, = and the corresponding Z=value from Appendix Table A-4 is 1.96.

11 3. r (1.96) 2 (1.4) 2 /(1) 2 = 7.7 Since this value is greater than 7, we will choose 8 as our sample size. As this formula is only an approximate rule, it is usual to use 2 for the Z-value in the above calculation. The difficulty in using this formula is to find a reasonable estimate of the population standard deviation. Now we will introduce an alternative method to estimate sample size without the explicit need for a population standard deviation. (CV); It is common to express variability among experimental units by the coefficient of variation CV = 100 / and is estimated by 100 S/ Y It defines variability in terms of the standard deviation expressed as a percentage of a mean. If we also express the required difference between a sample mean and the population mean as a percentage of the population mean, the formula for sample size determination becomes: r 4 (CV) 2 / (% ) 2 For the above example, suppose we do not have good estimates of the population parameters. The problem of determining sample size can be stated as follows: With a confidence of 95%, the experimenter wants to know how many plots are necessary so that the sample mean will not deviate from the true mean by more than 3%. He anticipates that experimental error in terms of CV will be 5% or less. In this case, therefore, r = 12. r = 4(5) 2 / (3) 2 = 11.2, SUMMARY 1. Type I and Type II errors: Test decision H 0 is true H 0 is false accept H 0 correct Type II error reject H 0 Type I error correct 2. The level of significance (α): The probability of rejecting a true null hypothesis, or the probability for obtaining a critical value, under the distribution of the null hypothesis, which determines acceptance or rejection of a null hypothesis. The level of significance of a critical value is indicated by the subscript on the symbol of the test statistic. If the hypothesis to be tested is one-tailed then is all on one side of the Z or t

12 distribution. If the test is two-tailed, then /2 is on each side of the distribution. Note in Appendix Table A-6 that probabilities on the top of the table are one-tailed (right hand side). 3. The power of a test, (1 - β): The probability of rejecting a false null hypothesis or the probability of drawing a correct conclusion when the null hypothesis is false. β is commonly used to denote the probability of committing a type II error, thus the power is symbolized as 1 - β. 4. One-tailed and two-tailed tests. H 0 Null Hypothesis H 1 Alternative Hypothesis Type of Test μ = μ 0 μ μ 0 two-tailed μ = μ 0 (or μ > μ 0 ) μ = μ 0 (or μ < μ 0 ) 5. Confidence Interval Estimation μ < μ 0 μ > μ 0 one-tailed one-tailed A statistical procedure to find a set of values (confidence limits) from samples, in the form of an interval to estimate the population parameters with a high degree of confidence that the procedure used is correct. (1 - α ) Confidence limits for a population mean Lower Limit (L) Upper Limit (U) small sample Y tα S y Y+ tα S y large sample Y Zα S y Y+ Zα S y large number of trials P\$ Zα S P \$ P\$ + Zα S P \$ (proportions) The interpretation of a (1 - α) confidence interval is that (1 - α) percent of the intervals constructed for all possible sample means of same sample size will contain the true mean, μ. The percent of the time that the interval will not contain the true mean, μ, is α. Thus, the probability (1 - α), applies to all the intervals that can be constructed from repeated samplings of a fixed size, while an interval estimate is calculated based on one sample result. For any specific interval estimate, we should use the work confidence instead of probability with the interval. 6. Hypothesis testing of a population mean. Test Statistic Standard Error Critical Value Reject H 0 if:

13 t = Y Z = Y μ S = S 2 y / n t α with df = n-1 t > t α S y μ S S y = 2 / n Z α for n > 30 Z > Z α S y Z P \$ = P σ P σ P = P( 1 P(/ n Z α for large number of trials. Z > Z α 7. Sample size determination: The following are two useful approximation formula to estimate sample size required for a study: r Z 2 α σ 2 / d 2 where α is a required confidence that the estimated mean will not differ from the true mean by more than a magnitude of d, and σ 2 is a previously known value of the population variance. CV r 4 2 ( ) 2 (% μ) where CV is the coefficient of variation and % represents the required precision that an estimated mean will be no more than a certain percentage greater than or smaller than the true mean μ. EXERCISES 1. A random sample of 10 items is observed from a normal population with the following values: 27, 25, 31, 23, 27, 37, 28, 30, 24, 29. What is the 95% confidence interval estimate of the population mean? Can you conclude that the population mean is greater than 25? (L=25.35, U=30.45, yes) 2. What are some of the advantages of using confidence intervals to estimate population parameters instead of point estimates? What are the disadvantages? 3. Explain the meaning of a type I and a type II error in terms of the concept of hypothesis testing. How is the level of significance related to the probability of committing a type I error? 4. give an example in your own field, where a one-tailed test is used. 5. What are the differences between a normal distribution and a t-distribution? Under what circumstances can a "t" be used? 6. How can we relate the concepts of interval estimation and hypothesis testing? State the types of errors that a confidence interval can cause. 7. In construction of a confidence interval, what kind of an error can we control? And what kind of an error are we unable to control? Explain.

14 8. What is the role of sample size in constructing a confidence interval? Illustrate your point with an example. 9. Suppose two scientists test the same hypothesis using the same data. Can they reach different conclusions? Assuming that the procedures used by the two scientists are correct, explain why they may reach different conclusions. 10. Suppose that a random sample of 8 subjects was taken from a normal population with a known standard deviation,, equal to 3. The sample mean is 30. Test the following hypothesis: H 0 : μ < 28 H 1 : μ > 28 At the 5% significance level. What is your conclusion for the hypothesis if the population standard deviation is unknown, but the sample estimate, S, is 3? (Z=1.89, t=1.89, df=7) 11. A steel manufacturing company wishes to know whether the tensile strength of the steel wire had an overall average of 125 pounds. A sample of 30 units of steel wire produced by the company yielded a mean strength of 120 pounds and a sample variance S 2 = 144 square pounds. Should the company conclude that the average strength is not 125 pounds with a = 5%? What is the 95% confidence interval for the true mean? (t=2.045, df=29) (CI= to ) 12. Experience shows that a fixed dose of a certain drug causes an average increase in pulse rate of 10 beats per minute. A group of 9 patients given the same dose showed the following increases: 13, 15, 14, 10, 8, 12, 16, 9, 20. a) Does the mean increase measured by this group differ significantly from that of the previous population, if the population standard deviation is known to be 4? (Z=2.25) b) If the population standard deviation of 4 is not known, answer the question asked in part a). (t=2.38, df=8, one-tailed) 13. The drained weights, in ounces, of a random sample of 8 cans of cherries are as follows: 12.1, 11.9, 12.4, 12.3, 11.9, 12.1, 12.4, a) Find the 99% confidence limits for the mean drained weight per can of cherries for the population from which the sample is taken. (CI is (11.90, 12.40)) b) Using the result obtained in part (a), does the sample indicate (1% level) that the production standard of ounces average drained weight per can of cherries is being maintained? Explain your answer. 14. The mean yield of wheat in Yolo County is 30 sacks/acre. A corporation has 9 fields of wheat in 2 the county with an average yield of 38 sacks/acre and with a sum of squares Σ( Yi Y) = 288. Test, using the 5% level, if the average yield of wheat for this corporation is significantly greater than the county average. (t=4.0, df=8, one-tailed)

15 15. A random sample of 25 capsules of a halibut liver oil product was tested for Vitamin A content. The mean amount (in 1000 international units per gram) was found to be 23.4 with an estimated standard deviation (S) of Find the 90% confidence limits for the mean of the population. (CI is (19.54, 27.26)) 16. Suppose that an entomologist is interested in the weights of bumble bees, so he catches 20 of these bees at random in a large alfalfa field in Davis and obtains the average weight 300 milligrams. From prior information, he knows the standard deviation of the weights is 50 mg. Compute and interpret the 95% confidence interval of the true mean weight of the bumble bees from the above information. (321.91, ) 17. Refer to problem 10, find the power of the test for the following cases: a) The true population mean is 30 and = 3. (1 - β) = 0.51 b) The true population mean is 31 and = 3. (1 - β) = c) The true population mean is 30 and S = 3. (1 - β) = 0.50 d) The true population mean is 31 and S = 3. (1 - β) = When can the Z-statistic be used to test hypotheses about populations? Why is the sample size important when estimating and testing proportions? 19. The following wheat-yield data (bushels/acre) are collected from 17 fields in Sacramento and central valleys of California. Column-2 shows the yield in 1984 and column 3 gives the yields in The difference of yields between the two years for each field is shown in column 4. Field 84 Yield 85 Yield Difference

16 x SD a) consider the 17 fields to be a random sample of all possible wheat fields in California. Can we conclude that the true mean yield in 1984 was 80 bushels/acre or better? (t=-1.36, df=16, one-tailed) b) From the data of column-4 construct a 95% confidence interval for the mean yield difference between the two years. Based on your confidence limits, will you reject or not reject a hypothesis that the mean difference is zero? At what significance level? (8.74, 2.46, α = 0.05) c) How many yield data should one collect so that the half length of a 95% confidence interval about the mean difference would not be greater than two bushels/acre? (n=36) d) Assume that the sample mean and standard deviation in column-2 are true population parameters. What is the probability that a randomly selected field in 1984 would have a yield between 75 and 80 bushels/acre? (0.185) 20. A survey of microcomputer owners suggested that about 40% of the microcomputers are IBM compatible machines. To verify the survey, Bill has asked 144 microcomputer owners in UCD and found that there were 3% IBM compatible machines. Determine whether the survey result of 40% IBM compatible machines was acceptable. Give your reasons. (Z= ) 21. Given a sample proportion, P, equal to 0.4, based on a random sample of 40 subjects: a) Construct a 99% confidence interval estimate of the population proportion, P. (CI is (0.19, 0.61)) b) Based on the above information, test the null hypothesis H 0 : P = 0.5 against the alternative hypothesis H 1 : P 0.5 at the 5% significance level. (Z=1.25) 22. Referring to the above problem, construct a 99% confidence interval for P, based on P = Is the confidence interval realistic? How would you correct the result? (CI is (-0.03, 0.05)) 23. In 144 rolls of a die, a 6 is obtained 32 times. Does this cast doubt on the honesty (balance) of the die? (Z=1.78)

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