# 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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