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Lecture

# UASTAT141Ch20_21.pdf

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School
University of Alberta
Department
Statistics
Course
STAT141
Professor
Paul Cartledge
Semester
Winter

Description
Ch. 20 - Hypotheses and Test Procedures Def’n: A null hypothesis is a claim about a population parameter that is assumed to be true until it is declared false. An alternative hypothesisis a claim about a population parameter that will be true if the null hypothesis is false. In carrying out a test of0H vs.AH , the hypothesis0H is “rejected” in favour oA H only if sample evidence strongly suggests that H0is false. If the sample does not contain such evidence, H0 is “not rejected” or you “fail to reject” it. NEVER “accept” H or0H …foA different reasons. Ex20.1) H : µ = 2.8 H : µ ≠ 2.8 0 A ↑ ↑ pop’n characteristic hypothesized value or “claim” Def’n: A two-tailed test has “rejection regions” in both tails. A one-tailed test has a “rejection region” in one tail. loweAr-ailted has the “rejection region” in the left tail. uppAer-ailted has the “rejection region” in the right tail. Ex20.2) a) H 0 µ = 15 HA: µ = 15 Æ INCORRECT b) H 0 µ = 123 HA: µ = 125 Æ INCORRECT c) H 0 µ = 123 HA: µ < 123 Æ CORRECT d) H 0 µ ≥ 123 HA: µ < 123 Æ CORRECT e) H 0 p = 0.4 HA: p > 0.6 Æ INCORRECT f) H 0 p = 1.5 HA: p > 1.5 Æ INCORRECT g) H 0 p= 0.1 HA: p ≠ 0.1 Æ INCORRECT Two-Tailed Test Lower-Tailed Test Upper-Tailed Test orSign for H0 == or ≥ = ≤ Sign for H ≠ < > A “Rejection region” In both tails In the left tail In the right tail Ex20.3) Is the mean different than 0? H 0: µ = µ0 H A: µ ≠ µ0 Is the mean lower than µ0? H 0: µ ≥ 0 H A: µ < µ0 Is the mean lower or still the sµ ? Hhan : µ ≤ µ H : µ > µ 0 0 0 A 0 Is the mean higher than µ0? H 0 µ ≤ µ0 H A: µ > µ0 Def’n: A test statistic the function of the sample data on which a conclusion to reject or fail to reject0H is based. For example, Z and t are test statistics. The P-value is a measure of inconsistency between the hypothesized value for a pop’n characteristic and the observed sample. Assuming H 0 is true, the P-value can be defined as the probability of obtaining a test statistic value at least as inconsisten0 with H as what actually resulted. Keep in mind that we want to be inconsistent with 0to reject it. Thus, the smaller the P-value, the more likely we reject H0. The significance level (denoted by α) is a number such that we reject H i0 the P- value is less than or equal to that number. The “significance level approach”: reject H0if p-value ≤ α do not reject H 0f p-value > α Common choices for α are 0.01, 0.05, and 0.1, depending on the nature of the test. PROBLEMS: a) If you’re comparing to α = 0.05, are the P-values 0.045 and 0.000 001 “different”? b) If we use a “cut-off” like α = 0.05, does it make sense to conclude differently between P-values of 0.049 and 0.051? Solution: ALWAYS report your P-value! That way a reader may draw their own conclusions. Moreover, use the “judgment approach” for rejection. Here, there’s a tendency of avoiding “cut-off” points and going toward some “acceptable” guidelines: 0.01 > P-value > 0 Æ strong to convincing evidence against H 0 0.05 > P-value > 0.01 Æ moderate to strong evidence against H 0 0.10 > P-value > 0.05 Æ suggestive to moderate evidence against H , ye0 inconclusive 1 > P-value > 0.1 Æ weak evidence against H 0 Steps of a Significance Test: 1. Assumptions: Specify variable/parameter. What assumptions apply? Do they hold? 2. Hypotheses: State the null/alternative hypotheses. (Select α for the test.) 3. Test statistic: Use the appropriate formula for the gi
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