Extending Hypothesis Testing. pvalues & confidence intervals


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1 Extending Hypothesis Testing pvalues & confidence intervals
2 So far: how to state a question in the form of two hypotheses (null and alternative), how to assess the data, how to answer the question by using a statistic and an associated measure of the probability of observing our statistic, given the current state or null hypothesis.
3 Next: We will use the pvalue to: make inferences about the population assign a level of confidence
4 Review the Steps Phase 1: State the Question 1. Evaluate and describe the data 2. Review assumptions 3. State the questionin the form of hypotheses Phase 2: Decide How to Answer the Question 4. Decide on a summary numbera statisticthat reflects the question 5. How could random variation affect that statistic? 6. State a decision rule, using the statistic, to answer the question
5 Detailed Steps (cont) Phase 3: Answer the Question 7. Calculate the statistic 8. Make a statistical decision 9. State the substantive conclusion Phase 4: Communicate the Answer to the Question 10. Document our understanding with text, tables, or figures
6 Clarify & Generalize the steps Step 2: Assumptions: Representative: Is the observed data representative of the population? Independence: Are the observations (responses of interest) independent? Size: Is the size of the sample large enough to make generalizations to the population at large?
7 Size Assumption So, how large is large enough? Ruleofthumb: N large enough to expect to see five of each of the two outcomes Both of the following must be true: p 0 n > 5 (1 p 0 ) n > 5
8 In the CPR study p 0 = 0.06 and n = 278, so: p 0 n = = > 5 (1 p 0 ) n = (1 0.06) 278 = > 5
9 Common Mistakes Using the observed proportion rather than the hypothesized proportion Compare the observed number of events of interest to five Why Not? We always operate under the assumption that the null hypothesis is true so use the null proportion!
10 Step 2 is particularly important: If the data do not meet the assumptions, then the statistical tests applied to test the hypothesis will not be valid Only proceed to steps 3 10 if the assumptions are met
11 Step 4 For the CPR example, we used a specific statistic, the proportion p The statistic and decision rules can be more generally defined and applied to all situations for testing a proportion.
12 Review CPR Simulation H0: The population survival proportion is 0.06 or less if the observed proportion p (x = 23 survivors or less). HA: The population survival proportion is larger than 0.06 if the observed proportion p > (x = 24 or more survivors).
13 Recall for the CPR simulations, the results looked similar to a normal distribution
14 Applied vs. Theoretical The smooth curve is the theoretical distribution of a normal curve under the null hypothesis Centered on the population value (p0 = 0.06) with proportions farther away from this center being less likely to occur Use the theoretical distribution to determine if our observed proportion is different from our assumed proportion
15 General Test Statistic observed p  hypothesized p standard error of the hypothesized p z = pˆ p0 p0 0 n ( 1 p )
16 observed proportion assumed proportion z = standard error of p 0 p pˆ p0 ( 1 ) 0 p0 n
17 Why p 0? Calculate the test statistic under the assumption that the null hypothesis is true. We are not concerned about how the variability of the observed data will affect our hypothesis testing result We believe the null hypothesis and the variability in the observed data should be assumed to be the same as the variability under the null hypothesis.
18 Zscore Using the zscore allows us to use a decision rule based on the standard normal distribution, rather than the proportion, p. The standard normal distribution ~N(0,1) The cutoff for the decision rule does not change for different values of p, n, and p 0.
19 For an a = 0.05, the z value is 1.645, ( 5% of the N(0,1) values are greater than 1.645)
20 General Decision Rule H 0 : proportion p p 0. Choose this if z z critical and pvalue α. H A : proportion p > p 0. Choose this if z > z critical and pvalue < α.
21 Clarify Steps w/ CPR Example 1. Evaluate and describe the data We observed n = 278 CPR patients who received instructions by phone, of whom x = 29 survived to hospital discharge. The characteristic of interest is survival proportion, p = 29/278 = The intent is to compare the outcomes in this study to a = 0.06 survival rate presumed to be typical.
22 2. Review assumptions There are three assumptions: Representativeness: From the design of the study, it is clear that subjects are representative of cardiacarrest victims in cities with a quickresponse emergency system. Independence: The response of one cardiacarrest victim does not depend on the response of others. The subjects are independent. Sufficient size: Since, n = = > 5, and (1 ) n = (1 0.06) 278 = > 5, this assumption is valid.
23 3. State the question in the form of hypotheses The intent is to show that phonecpr is superior to doing nothing. Thus, the alternative hypothesis is that there are higher than 6% survival rates: H 0 : p 0.06 H A : p > 0.06.
24 4. Decide on a summary number a statistic that reflects the question We ll use the zscore: z = pˆ p0 ( 1 p ) p0 0 n
25 5. How could random variation affect that statistic? If the null hypothesis is true, then z is zero. Since the assumptions are met, z is normally distributed. Large values of z reflect higher survival proportions and thus favor the alternative hypothesis.
26 6. State a decision rule, using the statistic, to answer the question General Choose to believe (at α = 0.05): H 0 : Choose this if pvalue α H A : Choose this if pvalue < α For CPR Example, for an α = 0.05: H 0 : p 0.06 Choose this if pvalue 0.05 H A : p > Choose this if pvalue < 0.05
27 7. Calculate the statistic z pˆ p = = p0 0 n 278 ( 1 p ) 0.06( ) = = Recall that a zvalue to the right of 3 is unlikely. In fact, the associated pvalue is p = (we ll talk about calculating pvalues later).
28 8. Make a statistical decision Reject the null hypothesis since pvalue < The observed value of the summary statistic is larger than what is expected by chance alone.
29 9. State the substantive conclusion We conclude that the survival proportion is larger than 0.06.
30 10. Document our understanding with text, tables, or figures Does dispatcherinstructed bystanderadministered CPR improve the chances of survival? Without this intervention it is presumed that the survival probability will be unchanged (at 6%). From this study, which used n = 278 patients, we observed p = (x = 29 survived until hospital discharge). The observed rate was compared to the hypothesized rate using the z test statistic. We reject the hypothesis p 0.06 in favor of the alternative hypothesis that the survival probability is larger than 6% (z = 3.09, pvalue = ).
31 Universal Decision Rule H 0 : nullhypothesis. Choose this if pvalue α (usually 0.05). H A : alternativehypothesis. Choose this if pvalue < α (usually 0.05).
32 How do we determine pvalues? pvalues can be determined from standard normal tables, such as Table A.1 in the Statistical Sleuth.,715 Tedious and you need to be careful what the table gives as the proportion it could be the opposite of what you are looking for! Use a calculator
33
34 Calculation note: Software might return a pvalue as 0 or not possible Determine the number of decimal places the calculator reports (when it will return a 0 value) Then report p < or p <
35 Confidence Intervals Often, researchers want to use a less rigid approach to hypothesis testing by estimating the parameter and placing upper and lower bounds (or limits) on the estimate. The interval is called a confidence interval.
36 The confidence interval approach allows us to make statements about a population parameter without referring to hypotheses Also gives a range of values that reflects our degree of certainty.
37 Definitions Inference: An inference is a conclusion that patterns observed in the data are present in the broader population. Statistical Inference: A statistical inference is an inference justified by a probability model (distribution) linking the data to the broader population. Parameter: A parameter is an unknown numerical value describing a feature of a distribution.
38 More Definitions Statistic: A statistic is any value that can be calculated from the observed data. Estimate: An estimate is a statistic used as a guess (or estimate) of a parameter.
39 General Definition estimate ± (reliability coefficient) (standard error) Estimating a parameter with an interval involves three components: The point estimate. The standard error of the estimate. This describes how much variability we expect. A reliability coefficient. This describes our degree of certainty.
40 Estimate Calculate the observed proportion: p = x / n In the CPR case p =
41 Standard Error The standard error we use here is different from that used in hypothesis testing. Recall that earlier we were in the mindset of hypothesis testing. Here we are not doing hypothesis testing here. We re just estimating a confidence interval based upon the observed data
42 Standard Error of phat SE p ˆ = pˆ ( 1 pˆ) n Note that the standard error of the estimate gets smaller as n gets larger. We expect less variability in an estimate if we use more data to make the estimate.
43 CPR Example For n = 278 and = 0.104, the associated standard error is: SE p ˆ pˆ ˆ = = n ( 1 p) 0.104( ) 278 =
44 Reliability coefficient The reliability coefficient reflects how sure we want to be: 95% sure 90% sure 99% sure Based on the standard normal for those proportions
45
46 Reliability Coefficients Commonly Used For 90% confidence, use z = For 95% confidence, use z = For 99% confidence, use z =
47 Confidence Interval pˆ z SE p ˆ ± ( 1 α 2) ( ) ± ( 0.068, 0.140)
48 Using a sentence: In the first case study there were 29 survivors (out of n = 278 studied) yielding a 95% confidence interval on the population survival proportion of [0.068, 0.140]. That is, We re 95% confident that the survival proportion is between and
49 Is there a 100% CI? Yes, it is [0,1] But this is a silly answer and doesn t make a conclusive statement about the population estimate. This is the same for all proportions!
50 Using the 10 Steps The Changes to the 10 Steps are minimal: 3. State the question (CI) 4. Decide on a summary statistic that reflects the question (CI formula). 5. How could random variation affect that statistic? (If the assumptions are met, then this interval will cover the population proportion 95% of the time )
51 6. Determine the reliability coefficient and standard error to be used in the CI 7. Calculate the interval 8. Compare the interval to comparison value (If there is a comparison value, does the interval include it?)
52 9. State the substantive conclusion: Something like: We estimate the population proportion of to be [lower, upper] with 95% confidence perhaps which does not include the hypothesized value of. 10. Document our understanding with text
53 Summary We have looked at several methods to assess and describe data and underlying populations. We can use simulations, zscores, pvalues, or confidence intervals about an estimate to make conclusions about observed data and broader populations. Next, we ll look at sample size and precision of estimates and the design of a study to estimate population proportions.
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