1 T-test in SPSS Hypothesis tests of proportions Confidence Intervals (End of chapter 6 material)
2 Definition of p-value: The probability of getting evidence as strong as you did assuming that the null hypothesis is true. A smaller p-value means that it s less likely you would get a sample like this if the null hypothesis were true. A smaller p-value means stronger evidence against that null hypothesis.
3 Definition of alpha, the level of significance: The highest acceptable p-value that we will use to reject the null hypothesis. The default alpha is A smaller alpha means less of a chance of falsely rejecting the null. (Also called a Type I error) A smaller alpha means we want to be more certain about something before rejecting the null.
4 If the p-value is smaller than the alpha, we reject the null hypothesis. (Enough evidence to reject) If the p-value is larger than the null, we fail to reject the null hypothesis. (Not yet enough evidence to reject)
5 SPSS Example: Milk dataset. In the milk data set, good milk had a calcium level of 20 mg/l. Bad milk, for our cases, will have something other than 20. Step 1: Load the milk dataset. First we re going to try goodcalc4, which is a sample of 25 bottles.
6 Our null hypothesis is that the milk is good (calcium is 20mg/L) The alternative is that the milk has a different calcium value. We re comparing the mean of one sample to a specific value (20), so we ll go to Analyze Compare Means One-Sample T Test
7 Choose good sample, n=25, 3 rd. Move this variable to the right by dragging or using the move arrow. Then, change the test value to 20. (The hypothesized mean)
8 Click OK You ll get two tables. The first is a data summary like what we ve seen before.
9 The sample size is 25 The sample mean is The sample standard deviation is The standard error of the mean is (t-score = if we were doing this by hand.) The second table is the result of the t-test
10 Sig. (2-tailed) is the p-value for a two sided test. We re doing a two-sided test (not equal to 20), so this works. p-value =.275 >.050 (default level of significance) So we fail to reject the null.
11 Let s try some bad milk, as found in lowclac2, and this time let s say we don t care if the milk has too much calcium.
12 Another possibility is to include the upper area in the null hypothesis. The analysis is exactly the same. (Both ways acceptable)
13 (SPSS steps are the same, but with bad sample, n=25) SPSS gives us the two-sided significance, but we only want one side. By symmetry, we use half of two-sided p-value to get the one-sided value. p-value =.016 / 2 =.008 <.05.
14 We reject the null hypothesis. The milk is bad. Two-tailed means two-sided. The tails are terms for the ends of a distribution.
15 Having two tails isn t normal.
16 But in statistics, the Normal has two tails. Everything we ve done so far with means (z-scores, t-tests) we can do with proportions as well. The only difference is in the standard error. P is the sample proportion.
17 For proportions we always use the z-score (and not the t- table). is population proportion.
18 For proportions we always use the z-score (and not the t- table). Z is still the number of standard deviations about the mean.
19 Example: ICBC (Insurance Corporation of British Columbia) is concerned about people not wearing seatbelts in cars. If fewer than 90% of drivers are wearing seatbelts, ICBC will start a campaign to pick that number back up.
20 They surveyed 500 people (by hidden camera to prevent response bias) and find 461 wear seatbelts. First, get the sample proportion P, and its standard error. P = 461 / 500 = 0.922
21 Next, get the z-score. We want the chance of getting a sample of this proportion or less if the true proportion is.90. This area will include the mean, so we ll use area between and then add 50%. Total Area = 96.78%, or.9678
22 Why look at area less than z=1.85? The alternative hypothesis was that seatbeat usage is less than We found seatbeat usage was actually HIGHER than.900, so there should be very little evidence against the null. Area less than 1.85:.9678 P-value is.9678 >.05 (default level of significance), So we fail to reject the null. Real life terms: No seatbeat intervention is necessary.
23 Always wear your seatbelt, you never know who s on the road.
24 Confidence intervals From our sample, the proportion of people using their seatbelts was P = It would be dishonest to say that the true proportion that uses seatbelts is = But that s our best guess from our sample, so it would be even more dishonest to say it s any other value either.
25 Instead of a single value, it would be a lot more honest to give an interval that captured most of that uncertainty of the value and say We re 95% confident that the true parameter (mean, proportion, whatever) is in this interval. The interval we gave would be the 95% confidence interval. (90%, 99% or other values are possible, but 95% is default, just like 5% significance.)
26 The standard confidence interval is centered about the sample proportion. means plus or minus. To get the upper end of the interval, use the plus. To get the lower end of the interval, use the minus.
27 is the true proportion and P is the sample proportion as before.
28 is the standard error. In this case of the proportion.
29 Z* Stands for the critical z-score. For a 95% interval, it s whatever z-score would put 95% in the middle. (Or 2.5% on either side)
30 Two ways to find z*. 1. Look for the appropriate area beyond in the z-table. 2. Look at the bottom of the t-table for the appropriate significance at df. (One-tailed) For 95%, the area beyond is 2.5%. Both ways will give you a critical z-score z* = 1.96.
31 In the case the seatbelts, the 95% confidence interval would be:
32 =.922 (1.96)(.0119).922 (1.96)(.0119) = = (1.96)(.0119) = =.945 The 95% confidence interval is (.899 to.945) Or , 19 times out of 20.
33 Subtle note: This doesn t mean there s 95% chance that the true proportion is in the interval (.899 to.945). is a fixed value, it s either in there or it isn t. We ve set an interval that has a 95% chance to contain the parameter.
34 Each blue vertical line is a confidence interval. The red dotted line horizontally across them represents the parameter value. Most blue lines cross the red line (include the parameter), but not all of them.
35 Confidence interval: Milk example. Find the 95% confidence interval of the calcium level in the good milk. From SPSS, we know the sample mean is and that the standard error of the mean is 1.082
36 The milk example uses a t* critical, because we re using the t- distribution. In t-table: One-tailed,.025 significance, df=24, the critical value is: (.050 sig. in two-tailed gives the same value)
37 We re 95% confidence that the true calcium level is between (2.064)(1.082) = = (2.064)(1.082) = = is between and Since the hypothesized value of 20 is within the confidence interval, 20 is a plausible value for the parameter. Just as before, we fail to reject the null hypothesis.
38 We can use the confidence intervals to do two-sided hypothesis tests as well. By-hand confidence interval rule: If the confidence interval contains the value given in the null hypothesis, we fail to reject. Otherwise, reject to contains 20, so we fail to reject
39 In SPSS, doing a one-sample t-test will automatically give you a confidence interval as well (defaults to 95%). The values given in SPSS are in relation to means = means = 21.02
40 SPSS does this because then the reject/fail to reject rule is simplified a bit: If the confidence interval includes zero, we fail to reject. Otherwise, reject. (-3.44 to 1.02 contains zero, so we fail to reject)
41 Final point: The confidence interval only works for two-sided tests. Why? The interval cuts off at both ends. It has a lower limit and an upper limit. That means if the sample value is too far above or too far below the null hypothesis value, it will be rejected. By definition that s a two-sided test.
42 On Monday, we do two-sample t-tests (start of chapter 7). Assignment 3 coming tonight or on the weekend.
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