Topic 15: Simple Hypotheses
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1 Topic 15: November 10, 2009 In the simplest set-up for a statistical hypothesis, we consider two values θ 0, θ 1 in the parameter space. We write the test as H 0 : θ = θ 0 versus H 1 : θ = θ 1. H 0 is called the null hypothesis. H 1 is called the alternative hypothesis. The possible actions are: Reject the hypothesis. Rejecting the hypothesis when it is true is called a type I error or a false positive. Its probability α is called the size of the test or the significance level. Fail to reject the hypothesis. Failing to reject the hypothesis when it is false is called a type II error or a false negative, has probability β. The power of the test 1 β. H 0 is true H 1 is true reject H 0 type I error OK fail to reject H 0 OK type II error Given observations X, the rejection of the hypothesis is based on whether or not the data X lands in a critical region C. Thus, reject H 0 if and only if X C. Given a choice α for the size of the test, the choice of a critical region C is called best or most powerful if for any choice of critical region C for a size α test, and we have the lowest probability of a type II error, β β. β = P θ1 {X / C}, β = P θ1 {X / C }. (1) 1 The Neyman-Pearson Lemma The Neyman-Pearson lemma tell us that the best test for a simple hypothesis is a likelihood ratio test. Theorem 1 (Neyman-Pearson Lemma). Let L(θ x) denote the likelihood function for the random variable X corresponding to the probability measure P θ, θ Θ. If there exists a critical region C of size α and a nonnegative constant k such that L(θ 0 x) k for x C and then C is the most powerful critical region of size α. k for x / C, L(θ 0 x) 95
2 2 Examples Example 2. Let X = (X 1,..., X n ) be independent normal observations with unknown mean and known variance σ 2. The hypothesis is H 0 : µ = µ 0 versus H 1 : µ = µ 1. The likelihood ratio L(µ 1 x) L(µ 0 x) = The likelihood test is equivalent to 1 (x1 µ1)2 1 (xn µ1)2 2 2σ 2 2 2σ 2 1 (x1 µ0)2 1 (xn µ1)2 2 2σ 2 2 2σ 2 = 1 n 2σ 2 (x i µ 1 ) 2 1 n 2σ 2 (x i µ 0 ) 2 = 1 2σ 2 ( (xi µ 1 ) 2 (x i µ 0 ) 2) = µ 0 µ 1 2σ 2 (µ 1 µ 0 ) (2x i µ 1 µ 0 ) x i k 1, or for some k α, known as the critical value for the test statistic x x k α when µ 0 < µ 1 or x k α when µ 0 > µ 1. To determine k α, note that under the null hypothesis, X is N(µ0, σ 2 /n) and X µ 0 σ/ n is a standard normal. Set z α so that P {Z z α } = α. Then, for µ 0 < µ 1, X µ 0 + σ n z α = k α. For µ 1 < µ 0, we have X k α. Equivalently, we can use the standardized score Z as our test statistic and z α as the critical value. The power should increase as a function of µ 1 µ 0, decrease as a function of σ 2, and increase as a function of n. In this situation, the type II error probability, β = P µ1 {X / C} = P µ1 { X < µ 0 + σ 0 z α } n { X µ1 = P µ1 σ 0 / n < z α µ } ( 1 µ 0 σ 0 / = Φ z α µ ) 1 µ 0 n σ 0 / n For µ 0 = 10 and µ 1 = 5 and σ = 3. Consider the 16 observations and choose a level α = 0.05 test, then 96
3 dnorm(x, y 0, 0.25) x dnorm(x, 0.75, y 0.25) x Figure 1: Top graph: Density of X for normal data under the null hypothesis - µ = 0 and σ/ n = With an α = 0.05 level test, the critical value k α = The area to the right of the red line and below the density function is α. Bottom graph: Density of X for normal data under the alternative hypothesis. µ = 3/4 and σ/ n = The probability of a type II error is β = The area to the left of the red line and below the density function is β. 97
4 > qnorm(0.05) [1] Thus the critical value is z α = for the test statistic Z. Not let s look at the data. > x [1] [7] [13] > mean(x) [1] Then / = z α = > and we fail to reject the null hypothesis. To compute the probability of a type II error, note that for α = 0.05, >> pnorm(-5.022) [1] e-07 z α µ 1 µ 0 σ 0 / n = / 16 = This is called the z-test. If n is sufficiently large, the even if the data are not normally distributed, X is well approximated by a normal distribution and, as long as the variance σ 2 is known, the z-test is used in this case. In addition, the z-test can be used when g( X 1,..., X n ) can be approximated by a normal distribution using the delta method. Example 3 (Bernoulli trials). Here X = (X 1,..., X n ) is a sequence of Bernoulli trials with unknown success probability θ, the likelihood ( ) x1+ +x θ n L(θ x) = (1 θ) n. 1 θ For the test the likelihood ratio L(θ 0 x) = H 0 : θ = θ 0 versus H 1 : θ = θ 1 ( ) n (( 1 θ1 θ1 1 θ 0 Consequently, the test is to reject H 0 whenever 1 θ 1 ) / ( θ0 1 θ 0 )) x1+ +x n x i k α when θ 0 < θ 1 or x i k α when θ 0 > θ 1. Note that under H 0, n X i has a Bin(n, θ) distribution. Thus, in the case θ 0 < θ 1, we choose k α so that k=k α ( ) n θ k k 0(1 θ 0 ) n k α. (6) In genreal, we cannot choose k α to obtain the sum α. Thus, we take the minimum value of k α to achieve the inequality in (6). 98
5 If nθ 0 is sufficiently large, then, by the central limit theorem, n X i has a normal distribution. If we standardize X θ 0 θ0 (1 θ 0 )/n is approximately a standard normal random variable and we perform the z-test as in the previous exercise. For example, if we take θ 0 = 1/2 and θ 1 > 1/2 and α = 0.05, then with 60 heads in 100 coin tosses > qnorm(0.95) [1] = 2. Thus, z 0.05 = < 2 and we reject the null hypothesis. Example 4. Now consider the hypotheses H 0 : p = 0.7 versus H 1 : p = 0.8. for a test of the probability that a feral bee hive survives a winter. The test statistic is X p p(1 p)/n and for an α level test, the critical value is z α where α is the probability that a standard normal is at least z α For this study, 112 colonies have been chosen and 88 survive. Thus X = and If the significance level is α = 0.05, then we will reject H 0. If, instead, we collect our data over two years, and use g( X) = X as our test statistic, then using the delta method, we have g( X) g(q) g (q) q(1 q)/n = X q 1/(2 q) q(1 q)/n = X q (1 q)/4n with q = p 2 = 0.49 under the null hypothesis. Thus, if 61 hives survive two years, and we fail to reject H 0. 99
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