Stat-403/Stat-650 : Intermediate Sampling and Experimental Design ...

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Chapter 6How do I interpret a p-valuep-values are prone to mis-interpretation as they measure the plausibility of thedata assuming the null hypothsis is true, not the probability that the hypothesisis true. There is also the confusion between selecting the appropriate p-valuefor one- and two-sided tests.This is a copy of the BC Ministry of Forest pamphlet on interpreting p-values.It is available at: interpreting the p-value 11 http://www.for.gov.bc.ca/research/biopamph/47
BIOMETRICSINFORMATION(You’re 95% likely to need this information)PAMPHLET NO. # 30 DATE: March 5, 1991SUBJECT:Interpretation of probability p-valuesMost statistical computer programs, including SAS, calculate and report probability or p-valuesin their output. These values can then be used to make conclusions about associated hypotheses.This pamphlet will discuss the use and interpretation of these p-values.For example, a fertilizer supplier claims that a new type of fertilizer can increase the averagegrowth rate of a certain type of tree. To test this claim, two random samples of these trees areselected and treated with the "new" fertilizer and the standard fertilizer respectively. The objectiveis to determine if the trees treated with the new fertilizer have a higher growth rate than the treestreated with the standard fertilizer.In hypothesis testing, two contradictory hypotheses are under consideration. The nullhypothesis H o states an equality between population parameters, for example, trees treated withthe new fertilizer have the same growth rate as trees treated with the standard fertilizer. Thealternate hypothesis H a states a difference between parameters and is usually what the experimenterhopes to verify, for example, trees treated with the new fertilizer have a better growth rate thantrees treated with the standard fertilizer. The null hypothesis will be rejected if the sampleevidence is more consistent with the alternative hypothesis.When drawing a conclusion in hypothesis testing, one is faced with the possibility of makingtwo types of errors. A Type I error consists of rejecting H o when it is true and a Type II errorinvolves not rejecting H o when it is false. The probability of making a type I error is traditionallydenoted by α , and the probability of making a type II error by β . Often the value α isspecified to indicate the amount of Type I error one is willing to tolerate. This α value isreferred to as the significance level of the test. Typically acceptable α-levels are 0.05 and 0.01.If, for example, one concludes that the growth rate of trees treated with the new fertilizer issignificantly different from those treated with the standard fertilizer at α = 0.05, then there is a 5%chance that the conclusion is wrong.While reporting the result of an hypothesis test, stating that H o is rejected at some α level isnot sufficient as it does not provide information on the weight of evidence against it. Also, itdictates the α-level that others must use. A more appropriate approach is to report the p-value:the smallest level at which the data is significant. H o is rejected at the α-level only if the p-valueis less than α . Suppose the p-value of the above fertilizer test is 0.02, that is, if one were toreject H o based on the data, then there is a 2% chance that the decision is incorrect. At α = 0.05,the experimenter would reject H o as the Type I error (2%) in the data is within the allowedamount of 5% specified. Conversely, at α 48 = 0.01, the experimenter would retain H o as theMinistry of ForestsResearch Program
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