# GGR270H1 Study Guide - Quiz Guide: Statistical Hypothesis Testing, Null Hypothesis, Means Test

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Statistics Lecture

November 16, 2011

•Non-parametric data: don’t have mean or standard deviation but

you are dealing with how many things are in a category

•Non-directional: there is a significant different between the

sample and the population miu is not equal to xbar

•Directional: xbar is significantly less than or greater than the

population; xbar= > or < miu

•Most important hypothesis testing error’s is a type one error

Hypothesis testing- Test Selection

•Test used is a function of the research question and research

assumptions

•Tests will vary according to the number of samples drawn,

sampling design and scale of information

•Ex. ttc might be testing how many people would ride the ttc

before a fare increase and post increase to see how their riding

patterns differ

•One of the most common is the One-Sample Means test or One

Sample Difference of Means Test

•Is there a significant difference between the sample mean and

the population mean

•Should always have significant difference in the hypothesis and

also state it in the conclusion

•You will either be using the z test or the t test [less than 30

sample size]

Z= xbar-miu

s/sqroot of n

t= xbar-miu

s/sqroot of n-1

•Placing a probability statement on the likelihood of sampling

error occurring

•We usually select a fairly low significance level in order to avoid

type 1 error [usually 0.05 or 0.01]

•Conclusion of the test is expressed in terms of the level of

significance of the result

•In classic hypothesis testing, rejective the null hypothesis at .05

is the same as saying the statistical test is significant at the .05

level

## Document Summary

Hypothesis testing- test selection: test used is a function of the research question and research assumptions sample size] In classic hypothesis testing, rejective the null hypothesis at . 05 is the same as saying the statistical test is significant at the . 05 level. Hypothesis testing- rejection regions: selecting a significance level allows the regions of rejection and non-rejection of the null hypothesis, entire set of values that a test statistic could assume, regions of rejection can be directional or non-directional. Calculating the test statistic: regardless of test used, a test statistic is always created, compare it to a critical value that we find with reference to our level of significance. In test comparing sample means, calculate a z or t statistic: choice will be driven by sample size, z for n> 30, t for n<30, ho: there is no significant difference, ha: there is a significant differene.