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Chapter 16: Inference in Practice STAT 1450 16.0 Inference in Practice Connecting Chapter 16 to our Current Knowledge of Statistics ▸ Chapter 14 equipped you with the basic tools for confidence interval construction. ▸ Chapter 15 equipped you with the basic tools for tests of significance. ▸ Chapter 16 addresses some of the nuances associated with inference (our owner’s manual of sorts). 16.1 Conditions for Inference Conditions for Inference ▸ Random sample: Do we have a random sample? If not, is the sample representative of the population? If not, was it a randomized experiment? ▸ Large enough population: sample ratio Is the population of interest at least 20 times larger than the sample? ▸ Large enough sample: Are the observations from a population that has a Normal distribution, or one where we can apply principles from a Normal distribution? Look at the shape of the distribution and whether there are any outliers present. 16.2 Cautions about Confidence Intervals Cautions about Confidence Intervals The margin of error covers only sampling errors. ▸ Undercoverage, nonresponse, or other biases are not reflected in margins of error. ▸ The source of the data is of utmost importance. ▸ Consider the details of a study before completely trusting a confidence interval. 16.2 Cautions about Confidence Intervals Example: Parental Monitoring Software ▸ Many parents elicit the use of various software and passwords to monitor the ways children use their computers. In a survey of a random sample of high school students, 16.7% with 3.45% margin of error expressed an ability to circumvent their parent’s security efforts. Would you trust a confidence interval based upon this data? Explain. The Confidence Interval would be (.1325, .2015). Yes, it is from a random sample. But, there is likely some under-reporting by the teens. As mentioned in Chapter 8, people tend to provide conservative answers to provocative questions. 16.3 Cautions about Significance Tests Cautions about Significance Tests ▸ When H0 represents an assumption that is widely believed, small p-values are needed. ▸ Be careful about conducting multiple analyses for a fixed a. It is preferred to just run a single test and reach a decision. 16.3 Cautions about Significance Tests Cautions about Significance Tests ▸ When there are strong consequences of rejecting H0 in favor of HA, we need strong evidence. ▸ Either way, strong evidence of rejecting H0 requires small p-values. ▸ Depending on the situation, p-values that are below 10% can lead to rejecting H0. ▸ Unless stated otherwise, researchers assume the de-facto significance level of 5%. 16.3 Cautions about Significance Tests Cautions about Significance Tests ▸ The P-value for a one-sided tests is half of the P-value for the two-sided test of the same null hypothesis and of the same data. ▸ The two-sided case combines two equal areas. The one-sided case has one of those areas PLUS an inherent supposition by the researcher of the direction of the possible deviation from H0. 16.3 Cautions about Significance Tests A Connection between Confidence Intervals and Significance Tests ▸ Analogous to how we use high levels of confidence for confidence intervals, we need strong evidence (and very small p-values) to reject null hypotheses. ▸ Standard levels of confidence are 90%, 95%, and 99%. ▸ Standard levels of significance are 10%, 5%, and 1%. Recall from last chapter: more than 10% was a “likely” event. 5% to 10% was an “unlikely” event. < 5% was an “extremely unlikely” event. 16.3 Cautions about Significance Tests Sample Size affects Statistical Significance ▸ Very large samples can yield small p-values that lead to rejection of H0. ▸ Phenomena that are “statistically significant” are not always “practically significant.” 16.3 Cautions about Significance Tests Example: Carry-on luggage ▸ Airlines are now monitoring the amount of carry-on luggage passengers bring with them. It is believed that the mean weight of carry-on luggage for passengers on multiple hour flights is 30 lbs. with a standard deviation of 7.5 lbs. A random sample of 500,000 passengers who had recently flown on multiple hour flights had an average carry-on luggage weight of 29.9 lbs. ▸ The test statistic is -9.43 with a P-value of 0. ▸ There is a statistically significant reason to reject the H0 and believe that the mean weight of carry-on luggage is not 30 lbs. But, practically, the sample mean (29.9) and the population mean (30.0) are quite comparable. 16.3 Cautions about Significance Tests Example: Carry-on luggage 𝑥 − 𝜇0 𝑧= = 𝜎 𝑛 16.3 Cautions about Significance Tests Example: Carry-on luggage 𝑥 − 𝜇0 29.9 − 30.0 𝑧= = 𝜎 𝑛 7.50 500000 16.3 Cautions about Significance Tests Example: Carry-on luggage 𝑥 − 𝜇0 29.9 − 30.0 𝑧= = = −9.43 𝜎 𝑛 7.50 500000 16.3 Cautions about Significance Tests Cautions about Significance Tests ▸ Be advised that it is better to design a single study and conduct one test of significance - (yielding one conclusion) than to design 1 study, and perform multiple analyses until a desired result is achieved. 16.4 Planning Studies: Sample Size for Confidence Intervals Sample Size for Confidence Intervals ▸ In Chapter 14, we noticed that the sample size impacts the margin of error (and thus the width of the confidence interval). ▸ The margin of error is expressed as 𝒙 ± 𝒎 where 𝒎 = 𝒛∗ 𝝈 𝒏 ▸ Some algebra leads us to a formula for the minimum sample size for a desired margin of error: The z confidence interval for the mean of a Normal population will have a specified margin of error m when the sample size is 𝑧∗𝜎 𝑛= 𝑚 2 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage ▸ In the carry-on luggage example from earlier, a random sample of 500,000 passengers yielded a standard deviation for the sample mean that was extremely small; resulting in |z| ≈ 9.43. Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage ▸ In the carry-on luggage example from earlier, a random sample of 500,000 passengers yielded a standard deviation for the sample mean that was extremely small; resulting in |z| ≈ 9.43. Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? 29.9 – 30 = | -.1| = .1= m 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? 𝟕. 𝟓 . 𝟏 = 𝒎 = 𝟗. 𝟒𝟑 29.9 – 30 = | -.1| = .1= m 𝟓𝟎𝟎𝟎𝟎𝟎 Larger n, smaller m. Quadrupling the sample size, divides margin of error in half.. Let’s try the inverse. 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? 𝟕. 𝟓 . 𝟏 = 𝒎 = 𝟗. 𝟒𝟑 29.9 – 30 = | -.1| = .1= m 𝟓𝟎𝟎𝟎𝟎𝟎 Larger n, smaller m. Quadrupling the sample size, divides margin of error in half.. Let’s try the inverse. If we desire to double the m (m=.20) we need one-fourth the sample size.. 𝑚 = 9.43 ∗ 7.5 500000(1 4 = 9.43 ∗ 7.5 125000 = .2 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? Larger n, smaller m. Quadrupling the sample size, divides margin of error in half.. Let’s try the inverse. If we desire to double the m (m=.20) we need one-fourth the sample size.. 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? Larger n, smaller m. Quadrupling the sample size, divides margin of error in half.. Let’s try the inverse. If we desire to double the m (m=.20) we need one-fourth the sample size.. 𝑚 = 9.43 ∗ 7.5 500000(1 4 = 9.43 ∗ 7.5 125000 = .2 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage Poll: Would you expect that ________ a) more or b) fewer passengers would need to be sampled to estimate the mean weight of carry-on luggage within a margin of error of 0.2 lbs. with 95% confidence? Larger n, smaller m. Quadrupling the sample size, divides margin of error in half.. Let’s try the inverse. If we desire to double the m (m=.20) we need one-fourth the sample size.. 𝑚 = 9.43 ∗ 9.43 ∗ 7.5 500000(1 4 7.5 125000 = 9.43 ∗ 7.5 125000 = .2 = 𝑚 = 1.96 ∗ Taking one-25th of 125,000 is about 5000. = .2 7.5 125000𝑐2 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage ▸ Explicitly determine the sample size. ∗ 𝑧 𝜎 𝑛= 𝑚 2 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage ▸ Explicitly determine the sample size. ∗ 𝑧 𝜎 𝑛= 𝑚 2 ∗ 1.96 7.5 = .2 2 16.4 Planning Studies: Sample Size for Confidence Intervals Example: Carry-on luggage ▸ Explicitly determine the sample size. ∗ 𝑧 𝜎 𝑛= 𝑚 2 ∗ 1.96 7.5 = .2 2 = 5402.25 ⇒ 5403 Resulting in a much smaller sample size. 16.5 Errors in Significance Testing Example: The Justice System Jury Verdict Truth about the Defendant Innocent Guilty Not Guilty Guilty 16.5 Errors in Significance Testing Example: The Justice System Jury Verdict Truth about the Defendant Innocent Correct decision Guilty Not Guilty Guilty Correct decision 16.5 Errors in Significance Testing Example: The Justice System Jury Verdict Truth about the Defendant Innocent Guilty Guilty Error Correct decision Not Guilty Correct decision Error 16.5 Errors in Significance Testing Power, Type I Error, and Type II Error Decision based on data Truth about a hypothesis Ho is true Reject Ho Fail to reject Ho Correct decision Ha is true 16.5 Errors in Significance Testing Power, Type I Error, and Type II Error Decision based on data Truth about a hypothesis Ho is true Reject Ho Fail to reject Ho Correct decision Ha is true Correct Decision 16.5 Errors in Significance Testing Power, Type I Error, and Type II Error Decision based on data Reject Ho Truth about a hypothesis Ho is true Ha is true Type I Error (𝛼 Correct Decision Fail to reject Ho Correct decision Type II Error (𝛽 16.5 Errors in Significance Testing Power, Type I Error, and Type II Error ▸ Type I Error – the maximum allowable “error” of a falsely rejected H0 (also the significance level, a). ▸ Type II Error – the probability of not rejecting H0, when we should have rejected it. ▸ Power – the probability that the test will reject H0 when the alternative value of the parameter is true. Note: Increasing the sample size increases the power of a significance test. ▸ Effect size – the departure from a null hypothesis that results in practical significance. 16.5 Errors in Significance Testing Example: Coffee consumption ▸ Recall the coffee consumption example from last chapter with standard deviation of 9.2 oz. A random sample of 48 people drank an average of 26.31 oz. of coffee daily. A significance test of the mean being different from our original estimate is conducted. Provide examples of a, b, and power. 16.5 Errors in Significance Testing Example: Coffee Consumption ▸ Recall the coffee consumption example from last chapter with standard deviation of 9.2 oz. A random sample of 48 people drank an average of 26.31 oz. of coffee daily. A significance test of the mean being different from our original estimate is conducted. Provide examples of a, b, and power. 𝛼: We reject H0 (𝜇 = “20”), when it was actually true. 𝛽: We fail to reject H0, when actually the true mean m was not “20.” Power: Probability of rejecting H0 for a specific value of 𝜇 ≠ 20. Five-Minute Summary ▸ List at least 3 concepts that had the most impact on your knowledge of inference in practice. ___________ _____________ _______________