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The Interpretation of hypothesis Testing

Understanding of what hypothesis testing is telling you.
by

Jermaine Wilkins

on 4 January 2013

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Transcript of The Interpretation of hypothesis Testing

Population to mean comparison is a common statistical method used
Once a hypothesis is generated, testing it becomes important
2 hypothesis are generated actually
Null
Alternative
Testing results are compared against the null hypothesis Experimental Design:
we will be measuring whether the IQ increase of children fed oily fish will deviate from the mean, assumed to be the normal condition
H0 = No increase. The children will show no increase in mean intelligence From IQ testing of the control group, you find that the control group has a mean IQ of 100 before the experiment and 100 afterwards, or no increase. This is the mean against which the sample group will be tested. The Interpretation of Hypothesis Testing The Interpretation of Hypothesis testing The Interpretation of Hypothesis testing example The Interpretation of Hypothesis testing The Interpretation of Hypothesis testing Hypothesis: Children have a higher IQ if they eat oily fish for a period of time
Alternate hypothesis H1: Children who eat oily fish for six months will show a higher IQ increase than children who have not
Null hypothesis H0: Children who eat oily fish for six months do not show a higher IQ increase than children who do not The children fed fish show an increase from 100 to 106
This appears to be an increase, but here is where the statistics enters the hypothesis testing process
we need to test whether the increase is significant, or if experimental error and standard deviation could account for the difference The Interpretation of Hypothesis testing Using an appropriate test, the researcher compares the two means, taking into account the increase, the number of data samples and the relative randomization of the group
A result showing that the researcher can have confidence in the results allows for the failure to reject the null hypothesis The Interpretation of Hypothesis testing Remember, not rejecting the null is not the same as accepting it
It is only that this particular experiment showed that oily fish had no affect upon IQ.
This principle lies at the very heart of hypothesis testing The Interpretation of Hypothesis testing Significance testing - is a statistical assessment of whether observations reflect a pattern rather than just chance The Interpretation of Hypothesis testing Experimental Error - errors that may occur in the execution of a statistical experiment design
Types of experimental error include:
human error
mistakes in data entry
systematic error
mistakes in the design of the experiment itself
random error caused by environmental conditions
other unpredictable factors
Experiment design seeks to minimize experimental error, in order to produce the most accurate data possible.
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