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# hypothesis testing

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University of Toronto St. George

Geography

GGR270H1

Damian Dupuy

Fall

Description

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 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-d

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