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Lecture 5

STATS 13 Lecture Notes - Lecture 5: Null Hypothesis, Statistic


Department
Statistics
Course Code
STATS 13
Professor
Tsiang, Mike
Lecture
5

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1.2 continued!
the 3s strategy!
statistic: compute the statistic from the observed data!
simulate: indentify a model that represents a chance explanation. repeatedly simulate values of the statistic that
could have happened when the chance model is true and form a distribution!
strength of evidence: consider whether the value of he observed statistic is unlikely to occur when the chance
model is true!
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the p value!
proportion of the simulated statistic in the null distribution that are at least as extreme (in the direction of the
alternative hypothesis) as the value of the statistic actually observed in the research study!
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general guideline but depends on the context !
i.e if its about drugs and health, want the p
value to be way smaller!
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rock paper scissor revisited!
what can we concluded!
we do not have strong evidence that few er than 1/3 of the time scissor is thrown!
but does this mean we can conclude 1/3 of the times scissors is thrown?!
what else is plausible!
what could we do to have a better chance of getting strong evidence for our alternative hypothesis!
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summary!
the smaller the p value the more strong the evidence!
the null hypothesis is the chance explanation!
alternative hypothesis is the explanation you're trying to show is true (<, >, =/=)!
null distribution is the distribution of simulated statistics the represent the chance outcome!
p-value is proportion of the simulated statistics in the null distribution that are at least as extreme as the value fo
the observed statistic!
the smaller the p value, the stronger the evidence against the null !
a p value less than 0.05 provides strong evidence against the null!
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notation summary!
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