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WMI 606: Research Methods Course
Hypothesis setting and testingsession 4
Thumbi Mwangi
1Paul G. Allen School for Global Animal Health, Washington State University2KEMRI - Center for Global Health Research
3Wangari Maathai Institute, University of Nairobi
February 11th, 2015
WMI 606: Research Methods Course
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Introduction toHypothesis setting and
testing
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http://www.sagepub.com/upm-
data/40007 Chapter8.pdf
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Definitions:
Hypothesis: an “educated guess” basedon prior knowledge or observation,explaining a phenomenon
Statement or prediction of therelationship between two or more factors
Provisional explanation of something -needs to be proved!
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.......the jury analogy........
A person is assumed innocent until proven guilty
The jury decides if the defendant is guilty or not guilty
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.......the jury analogy........
The decision NOT on whether guilty or innocentThe prosecutor must present evidence in a trial that shows thedefendant is guiltyThe evidence either shows guilt (decision: guilty) or does not(decision: not guilty).
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.....hypothesis setting........
review available evidence before setting ahypothesis
what do you think is true based on the availableevidence?
show logic in your hypothesis
include the “why” in your hypothesis
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Hypothesis testing: method for testing aclaim/hypothesis about a parameter in apopulation, using data measured in thesample
the purpose is to rule out chance(sampling error) as a plausibleexplanation for the study results
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.....determine whether a claim is true.........
The average height of Kenyan men is 5 feet 3inch
the population mean
You select a sample of Kenyan men andcalculate their average height = 5 feet 8 inch
the sample mean
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.....2 plausible explanations?.....
There two possible explanations for the observeddifferences:
1 the difference between the sample mean andthe population mean is due to Samplingerror
2 the difference between the sample mean andpopulation mean is too large to beexplained by Sampling error
the claim on average height of Kenyan men isnot supported by your data
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......which of the 2 plausible explanations do we go with...
The hypothesis to be tested is given thesymbol H0, and commonly referred to asthe Null Hypothesis
it is assumed to be true, unless there isstrong enough evidence against it
is the average height of Kenyans 5 feet3 inch?
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.....is there a treatment/intervention effect?....
In experiments - we are interested in knowing:
Does the treatment under investigation havean effect on the population mean?
Take an example:
To determine the impact of communityfocus group trainings on environmentalconservation on environmental governanceindices
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.....is there a treatment/intervention effect?....
The treatment:
Training of communities on mattersenvironmental conservation (through focusgroups)
The outcome:
Increase or decrease in the environmentalgovernance indices
Comparing:
Governance index score in communities nottrained (the population mean) and thosetrained (the sample mean)
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.....is there a treatment effect....
Does the training significantly affect the meanof the populations governance index?
or are observed differences (between populationand sample means) the result of sampling error?
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.....is there a treatment effect....
Does the training significantly affect the meanof the populations governance index?
or are observed differences (between populationand sample means) the result of sampling error?
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.....is there a treatment effect ....
The NULL hypothesis?
Focus group trainings on environmentalconservation do not have an effect ongovernance indices
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.....reading on hypothesis testing....
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.....hypothesis testing .......
a) Step1: select the “cut-off” point - the
level of significance; the criterion for
making a decision about the null
hypothesis
α = 0.05,
α = 0.01,
α = 0.001.
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.....hypothesis testing step1: select the “cut-off” point....
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.....hypothesis testing .......
b) Step 2: Identify the “critical region”
outcomes that are very unlikely to occur
if the null hypothesis is true
Governance indices (of the sample) that
are very unlikely to occur/to be observed
if focus group trainings on environmental
conservation have no effect
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.....hypothesis testing .......
b) Step 2: Identify the “critical region”
outcomes that are very unlikely to occur
if the null hypothesis is true
Governance indices (of the sample) that
are very unlikely to occur/to be observed
if focus group trainings on environmental
conservation have no effect
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.....critical region for α level 0.05....
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....computing the test statistic.......
c) Compute the test statisticto determine how far, how many standarddeviations - a sample mean is from thepopulation mean
The standard deviation: the dispersion of a set of data fromthe mean
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....computing the test statistic.......
For a normal distribution:
68% percent of the distribution is within
1 standard deviation.
95.4% within 2 standard deviations
over 99% within 3 standard deviations.
Mostly scientists will use the level of 2
standard deviations to make a decision
whether to reject or retain the null
hypothesis
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....computing the test statistic.......
For a normal distribution:
68% percent of the distribution is within
1 standard deviation.
95.4% within 2 standard deviations
over 99% within 3 standard deviations.
Mostly scientists will use the level of 2
standard deviations to make a decision
whether to reject or retain the null
hypothesis
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....reject or retain the null hypothesis?.......
d) Decision to reject or retain the nullhypothesis test statistic
A large value of the test statistic shows themean difference is more than would beexpected if there was no treatment effect
If the value falls within the critical region,the conclusion is the difference is significant
i.e the focus group trainings have an effecton governance indices
Null hypothesis is rejected
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....reject or retain the null hypothesis?.......
The probability of obtaining a samplemean, given that the value stated in thenull hypothesis is true, is stated by thep-value.
The p-value for obtaining a sampleoutcome is compared to the level ofsignificance - set in stage 2
When the p-value is < 0.05 - reject theNULL hypothesis
When the p-value is > 0.05 - retain theNULL hypothesis
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.....Errors with hypothesis testing....
Differences in the sample mean and thepopulation mean may not always be due to atreatment effect
Sampling error may cause such differences
There is the risk that misleading data may causethe hypothesis test to reach a wrong conclusion
Two possible types of errors:
Type 1 errorType 2 error
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.....Type 1 error....
Type I error
Sample data shows a treatment effect,where in fact, there is none!You reject the NULL hypothesis - a falseconclusion
Causes:
Researcher selected an extreme sample -that already falls in the critical regionE.g a sample of basketball players in order totest the hypothesis the average height ofKenyans is 5 feet 3 inches
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.....Type 1 error....
Type I error
Sample data shows a treatment effect,where in fact, there is none!You reject the NULL hypothesis - a falseconclusion
Causes:
Researcher selected an extreme sample -that already falls in the critical regionE.g a sample of basketball players in order totest the hypothesis the average height ofKenyans is 5 feet 3 inches
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.....Type 2 error....
Type II error
Sample data does not show a treatmenteffect, where in fact, the treatment doeshave an effect!You FAIL to reject the NULL hypothesis - afalse conclusion
Causes:
Very small treatment effectsAlthough the treatment has effect, its notlarge enough to be picked by the study
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.....Type 2 error....
Type II error
Sample data does not show a treatmenteffect, where in fact, the treatment doeshave an effect!You FAIL to reject the NULL hypothesis - afalse conclusion
Causes:
Very small treatment effectsAlthough the treatment has effect, its notlarge enough to be picked by the study
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.....reading on hypothesis testing....
http://www.sagepub.com/upm-data/40007 Chapter8.pdf