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Topic 7 Making inferences about Means General process of making inference about a statistic (1) Establish the sampling distribution of the statistic to assess the variability of the statistic. For example, if we are interested in the mean reading score of students in Victoria, we take a sample and compute the sample mean. Because this sample mean is not the population mean, there is likely variation in the value of the sample mean if different samples are drawn. We need to find out how large the variation is. If the variation is large, then our estimate is probably not very accurate to represent the population mean. If the variation is small, then our estimate is probably quite close to the population mean. (2) We can repeatedly sample to establish the sampling distribution of the statistic of interest. But this is impractical as it will be too costly. Making inferences about Mean We can use the central limit theorem to establish the sampling distribution of the sample mean, without doing repeated sampling. Central limit theorem says that the mean of independently drawn observations will be approximately normally distributed, even if the distribution from which the sample is drawn is not normal. Further, it can be shown that mean values computed from samples of size n have a normal distribution with mean and standard deviation of n (known as the standard error), where is the mean of the distribution we draw our samples from, and is the standard deviation of the distribution we draw our samples from. That is, if X denotes the sample mean, then X has a standard normal distribution n with mean zero and standard deviation 1 (z-score). For a standard normal distribution, 95% of the observations lie within 1.96. With a little re-arrangement of the equation, it can be shown that 95% of the time, or, we are 95% confident that, X 1.96 n X 1.96 n (There is a 95% chance that the population mean lies within the range shown above.) t-distribution Of course, in practice, we do not know the population standard deviation, . So we use the sample standard deviation, s , instead. X (where s is an estimate of ) is no longer normally s n distributed. Instead, it has a t-distribution, with n 1 degrees of freedom. This means However, the statistic that, we cannot use 1.96 for 95% confidence interval. We must find the 95% range from a t-distribution. The t-distribution approaches the normal distribution as the sample size increases. To compute the probability values from a t-distribution, use the EXCEL function TDIST. For example, in a cell in EXCEL, type =TDIST(1.96,50,2) The value 0.056 is returned. This means that 5.6% of the observations from a tdistribution (with 50 degrees of freedom) lie outside the values 1.96. In comparison, for the normal distribution, 5% of the observations lie outside the values 1.96. Conversely, the function =TINV(0.05, 50) returns the value 2.01. This means that 95% (=1-0.05) of the observations from a tdistribution with 50 degrees of freedom lie within 2.01 (instead of 1.96 as in the normal distribution). (See page 268, Coladarci, for a comparison between t-distribution and normal distribution) Example 1 In a sample of 50 randomly selected teachers, the average age is 36 with a standard deviation of 6. What is the 95% confidence interval for the population mean age of teachers? (Hint: What is the standard error of the mean? The 95% confidence interval for the population mean is Sample mean t(0.05, 49)×standard error, where t(0.05, probability, with 49 degrees of freedom.) 49) is the t-value for 95% Example 2 The cost of 15 randomly selected university textbooks at one university are (in dollars): 120 89 65 135 145 110 160 50 115 128 99 95 105 120 75 Estimate the mean cost of textbooks based on this sample at this university. Give 95% confidence interval. (1) Paste the prices in EXCEL. Compute average and standard deviation of the prices. Compute standard error. What is the 95% confidence interval? (2) Paste the numbers in SPSS. Select Analyse -> Descriptive statistics -> Explore. Compare the answers from SPSS with the results from EXCEL. Example 2B According to university administration, the average cost of textbooks is $90. Using the data from Example 2, at 95% confidence level, will you reject the hypothesis that the average cost is $90? What if you test this hypothesis at 90% confidence interval? What if you test this at 99% confidence interval? (Use SPSS explore to work out the confidence interval at different levels.) Two-sample t-test Example 3 At another university, a similar survey is carried out. The cost of 15 randomly selected university textbooks at this university are (in dollars): 100 120 145 120 115 118 66 89 93 89 109 120 60 70 55 We want to compare whether the cost of textbooks at university 2 is, on average, cheaper than the cost of textbooks at university 1. Paste the prices in EXCEL. Compute average and standard deviation of the prices. Based on the average and standard deviation, do you think the cost of textbooks at university 2 is generally cheaper? Did you make this judgement by comparing the average cost at university 1 and university 2? Did you take into account of the confidence intervals based on the estimates? Since there are only 15 textbooks in the sample, we expect variability in average cost from sample to sample. A formal statistical method to compare the means from two independently drawn samples is the two-sample t-test. The test takes into account the size of the difference between the two group averages, as well as the likely variability in the averages due to sampling. SPSS exercise Import the data for textbooks into SPSS (animated demo: TwoSample-t-test_demo.swf) (animated demo: t-test output_demo.swf) That is, there is about 1 in 3 chance (0.37) for us to observe a difference as large as $9.50 (= $107.4 - $97.9) when there is no difference in the ‘true’ (population) means. So we can say the following: At 63% confidence level (or lower), we will reject the hypothesis that the population means are the same. (Generally, 63% confidence level is not regarded as very confident!) At any confidence level higher than 63% (e.g., 90%, 95%, 99%), we cannot reject this hypothesis. You need to choose a confidence level before making your conclusions. Discussion point: First, note that different conclusions can be reached depending on the confidence level. Second, how does one decide on the confidence level? Third, once a conclusion is made, beware that the conclusions is NOT the ‘truth’ (of whether there IS a difference in the population means.). In fact, the population means are extremely unlikely to be identical to 100 decimal places, so the ‘truth’ is almost certainly that the mean cost at university 1 is NOT the same as the mean cost at university 2. Given large enough sample sizes, we will almost certainly find statistical significance, at high confidence levels, even if the population mean difference is minute. Effect size In addition to statistical significant, we may want to assess whether the observed difference matters. We might decide, for example, if the population mean difference is less than $2, then we will conclude that there is no “important” difference, and, regardless of statistical significance or not, we will conclude there is no difference. A commonly used measure of effect size is to look at the size of the difference in means in relation to the standard deviation: X1 X 2 standard deviation (pooled) (see page 295-298, Coladarci, et al) Example 4 Use the data set StudentLiteracyScoresCutDown.sav to assess whether there is a difference between the average reading scores for males and females. Use the whole data first. This is already a sample from the population. What conclusions have you drawn? How would you write your conclusion in a report? Sub-sample from this data set. At about what sample size, are you likely to change your conclusion? If you are reporting this to the minister of education, or reporting it as a newspaper article, how would you write it? Paired sample t-test In the examples about the cost of university textbooks, our survey method was not very efficient, because it could be by chance that different textbooks are selected at the two universities. The expected variation in the average cost may fluctuate more widely when different textbooks are selected. A better design is to choose the same textbooks at both universities and compare the prices, pairwise. The following shows prices of 15 textbooks as sold at two universities. text book 1 text book 2 text book 3 text book 4 text book 5 text book 6 text book 7 text book 8 text book 9 text book 10 text book 11 text book 12 text book 13 text book 14 text book 15 price at uni 1 120 89 60 125 150 115 160 65 115 128 100 98 105 120 80 price at uni 2 120 85 60 120 145 118 155 60 108 125 100 100 105 120 78 To compare the difference in average prices at the two universities, (1) Analyse as two independent sample t-test (You have to organise the data in SPSS with one column stacked under the other, and add a group variable with values of 1 or 2 to indicate which university it is.) (2) Analyse as paired-samples t-test (You have to organise the data in SPSS with two matched columns. See animated demo: Paired Sample t-test_demo.swf). What are the differences in results between the above two analyses? Why are they different? Exercise Use the data set, TIMSS2003AUS_Cutdown.sav, (1) Investigate whether there is a difference between students’ mathematics score and science score (variables 42 and 43, bsmstdr and bssstdr). Carry out analyses using (a) two sample t-test and (b) paired-sample t-test. Can you give results regarding statistical significance, and also say something about the importance (or otherwise) about the magnitude of the difference (concept of effect size). (2) Investigate whether there is any difference in mathematics achievement between girls and boys (use variable 42).