STA 210 Lecture Notes - Lecture 7: Statistical Significance, Null Hypothesis, Statistical Hypothesis Testing

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15 Apr 2017
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The goal of a hypothesis testing is to choose between:
HO : Treatment is not effective
HA: Treatment is effective
To understand how to make an informed choice, it helps to understand how hypothesis
testing is similar to a screening test
Think of HO as a “negative” outcome and HA as a “positive outcome”
Type 1 and Type 2 Errors
Type 1 Error
A false positive is when the results from a hypothesis test suggest that HA is true,
when in fact HO is true. It is called a type 1 error in statistical science
Type 2 Error
A false negative is when the results from the hypothesis test suggest that HO is
true, when in fact HA is true. It is called type 2 error in statistical science
Hypothesis testing amounts to a screening test that chooses between a null hypothesis and
alternative hypothesis based on a rule dictated by the type 1 error rate
Statistical Significance
“Significance” in the statistical sense does not mean “important.” It means “not likely to
happen by chance”
Statistical significance: when differences in treatments are sufficiently large that they
are unlikely to be due only to chance
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