This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: 5 steps to conducting a significance test: Significance test- a method of using data to summarize the evidence about a hypothesis. For a categorical variable the parameter is a proportion and for a quantitative variable the parameter is a mean 1. Assumptions- the data produced using randomization. Also assumptions about the population size and assumptions about the shape of the population distribution. What kind of sample? 2. Hypothesis- a statement about a population, usually of the form that a certain parameter takes a particular numerical value or falls in a certain range of values. Null (statement that t the parameter takes on a particular value) or alternative (parameter falls in some “range” of value) 3. Test statistic- describes how far the estimate falls from the parameter value given in the null hypothesis = ( - )/ se po 1 po n = = / ( / )/ se 1 3 2 3 116 = 0.0438 = - z p pose = .- / . z 0 345 1 3 0438 = .26 =( - )/ t x μo se = x = mean change Example- In the actual experiment, the astrologers were correct with 40 of their 116 predictions (a success rate of .345). The sample proport ion of .345 is only .26 standard errors above the null hypothesis value of 1/3. 4. P-value- is the probability that the test statistic equals the observed value or a value even more extreme. Summarizes how far out in the tail the test statistic falls by the tail probability of that value and values even...
View Full Document
- Spring '08
- Statistics, Null hypothesis, Statistical hypothesis testing, Significance test