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

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Published on 30 Nov 2011
School
UTSG
Department
Geography
Course
GGR270H1
Professor
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
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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.

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