Hypothesis Tests One Sample Means

A hypothesis test will allow me to decide if the claim is true or not! How can I tell if they really are underweight? A government agency has received numerous complaints that a particular restaurant has been selling underweight hamburgers. The restaurant advertises that it’s patties are “a quarter pound” (4 ounces). A hypothesis test will allow me to decide if the claim is true or not! Take a sample & find x. But how do I know if this x is one that I expect to happen or is it one that is unlikely to happen?

Steps for doing a hypothesis test “Since the p-value < (>) a, I reject (fail to reject) the H0. There is (is not) sufficient evidence to suggest that Ha (in context).” Assumptions Write hypotheses & define parameter Calculate the test statistic & p-value Write a statement in the context of the problem. H0: m = 12 vs Ha: m (<, >, or ≠) 12

Assumptions for t-inference Have an SRS from population (or randomly assigned treatments) s unknown Normal (or approx. normal) distribution Given Large sample size Check graph of data Use only one of these methods to check normality

Formulas: s unknown: m t =

Calculating p-values For z-test statistic – For t-test statistic – Use normalcdf(lb,ub) [using standard normal curve] For t-test statistic – Use tcdf(lb, ub, df)

Draw & shade a curve & calculate the p-value: 1) right-tail test t = 1.6; n = 20 2) two-tail test t = 2.3; n = 25 P-value = .0630 P-value = (.0152)2 = .0304

Example 1: Bottles of a popular cola are supposed to contain 300 mL of cola. There is some variation from bottle to bottle. An inspector, who suspects that the bottler is under-filling, measures the contents of six randomly selected bottles. Is there sufficient evidence that the bottler is under-filling the bottles? Use a = .1 299.4 297.7 298.9 300.2 297 301

What are your hypothesis statements? Is there a key word? SRS? I have an SRS of bottles Normal? How do you know? Since the boxplot is approximately symmetrical with no outliers, the sampling distribution is approximately normally distributed Do you know s? s is unknown What are your hypothesis statements? Is there a key word? H0: m = 300 where m is the true mean amount Ha: m < 300 of cola in bottles p-value =.0880 a = .1 Plug values into formula. Compare your p-value to a & make decision Since p-value < a, I reject the null hypothesis. Write conclusion in context in terms of Ha. There is sufficient evidence to suggest that the true mean cola in the bottles is less than 300 mL.

Example 2: The Degree of Reading Power (DRP) is a test of the reading ability of children. Here are DRP scores for a random sample of 44 third-grade students in a suburban district: (data on note page) At the a = .1, is there sufficient evidence to suggest that this district’s third graders reading ability is different than the national mean of 34?

I have an SRS of third-graders Normal? How do you know? Since the sample size is large, the sampling distribution is approximately normally distributed OR Since the histogram is unimodal with no outliers, the sampling distribution is approximately normally distributed Do you know s? What are your hypothesis statements? Is there a key word? s is unknown H0: m = 34 where m is the true mean reading Ha: m ≠ 34 ability of the district’s third-graders Plug values into formula. p-value = tcdf(.6467,1E99,43)=.2606(2)=.5212 Use tcdf to calculate p-value. a = .1

Compare your p-value to a & make decision Conclusion: Since p-value > a, I fail to reject the null hypothesis. There is not sufficient evidence to suggest that the true mean reading ability of the district’s third-graders is different than the national mean of 34. Write conclusion in context in terms of Ha. A type II error – We decide that the true mean reading ability is not different from the national average when it really is different. What type of error could you potentially have made with this decision? State it in context.

What do you notice about the hypothesized mean? What confidence level should you use so that the results match this hypothesis test? 90% Compute the interval. What do you notice about the hypothesized mean? (32.255, 37.927)

Example 3: The Wall Street Journal (January 27, 1994) reported that based on sales in a chain of Midwestern grocery stores, President’s Choice Chocolate Chip Cookies were selling at a mean rate of $1323 per week. Suppose a random sample of 30 weeks in 1995 in the same stores showed that the cookies were selling at the average rate of $1208 with standard deviation of $275. Does this indicate that the sales of the cookies is lower than the earlier figure?

What is the potential error in context? Assume: Have an SRS of weeks Distribution of sales is approximately normal due to large sample size s unknown H0: m = 1323 where m is the true mean cookie sales Ha: m < 1323 per week Since p-value < a of 0.05, I reject the null hypothesis. There is sufficient evidence to suggest that the sales of cookies are lower than the earlier figure. What is the potential error in context? What is a consequence of that error?

Example 3: President’s Choice Chocolate Chip Cookies were selling at a mean rate of $1323 per week. Suppose a random sample of 30 weeks in 1995 in the same stores showed that the cookies were selling at the average rate of $1208 with standard deviation of $275. Compute a 90% confidence interval for the mean weekly sales rate. CI = ($1122.70, $1293.30) Based on this interval, is the mean weekly sales rate statistically less than the reported $1323?

A special type of t-inference Matched Pairs Test A special type of t-inference

Matched Pairs – two forms Pair individuals by certain characteristics Randomly select treatment for individual A Individual B is assigned to other treatment Assignment of B is dependent on assignment of A Individual persons or items receive both treatments Order of treatments are randomly assigned or before & after measurements are taken The two measures are dependent on the individual

Is this an example of matched pairs? 1)A college wants to see if there’s a difference in time it took last year’s class to find a job after graduation and the time it took the class from five years ago to find work after graduation. Researchers take a random sample from both classes and measure the number of days between graduation and first day of employment No, there is no pairing of individuals, you have two independent samples

Is this an example of matched pairs? 2) In a taste test, a researcher asks people in a random sample to taste a certain brand of spring water and rate it. Another random sample of people is asked to taste a different brand of water and rate it. The researcher wants to compare these samples No, there is no pairing of individuals, you have two independent samples – If you would have the same people taste both brands in random order, then it would be an example of matched pairs.

Is this an example of matched pairs? 3) A pharmaceutical company wants to test its new weight-loss drug. Before giving the drug to a random sample, company researchers take a weight measurement on each person. After a month of using the drug, each person’s weight is measured again. Yes, you have two measurements that are dependent on each individual.

Stroop Test Is there an interaction between color & word? Or in other words … is there a significant increase in time?

A whale-watching company noticed that many customers wanted to know whether it was better to book an excursion in the morning or the afternoon. To test this question, the company collected the following data on 15 randomly selected days over the past month. (Note: days were not consecutive.) You may subtract either way – just be careful when writing Ha Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Morning After-noon Since you have two values for each day, they are dependent on the day – making this data matched pairs First, you must find the differences for each day.

-1 -2 I subtracted: Morning – afternoon Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Morning After-noon Differences -1 -2 I subtracted: Morning – afternoon You could subtract the other way! Assumptions: Have an SRS of days for whale-watching s unknown Since the normal probability plot is approximately linear, the distribution of difference is approximately normal. You need to state assumptions using the differences! Notice the granularity in this plot, it is still displays a nice linear relationship!

Differences -1 -2 1 2 Is there sufficient evidence that more whales are sighted in the afternoon? Be careful writing your Ha! Think about how you subtracted: M-A If afternoon is more should the differences be + or -? Don’t look at numbers!!!! If you subtract afternoon – morning; then Ha: mD>0 H0: mD = 0 Ha: mD < 0 Where mD is the true mean difference in whale sightings from morning minus afternoon Notice we used mD for differences & it equals 0 since the null should be that there is NO difference.

finishing the hypothesis test: Differences -1 -2 1 2 finishing the hypothesis test: Since p-value > a, I fail to reject H0. There is insufficient evidence to suggest that more whales are sighted in the afternoon than in the morning. In your calculator, perform a t-test using the differences (L3) Notice that if you subtracted A-M, then your test statistic t = + .945, but p-value would be the same How could I increase the power of this test?