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BIOL 2060 (22)


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York University
BIOL 2060
Joel Shore

Hypothesis testing  Hypothesis testing: compares data to the expectations of a specific null hypothesis. Is the data are too unusual, assuming that the null hypothesis is true, then the null hypothesis is rejected. 6.1 making and using hypotheses  Null hypothesis: specific claim about the value of a population parameter, made for the purpose of argument. Good one is a statement that would be interesting to reject.  Alternative hypothesis testing: represents all other possible parameter values except that stated in the null hypothesis.  Ho and Ha 6.2 Hypothesis testing: an example  two sided test: the alternative hypothesis includes values on both sides of the value specified by the null hypothesis.  Test statistic: quantity calculated from the data that is used to evaluate how compatible the results are with those expected under the null hypothesis  Null distribution: sampling distribution of outcomes for a test statistic under the assumption that the null hypothesis is true.  p-value: probability of obtaining the data (or data showing as great or greater difference from the null hypothesis) if the null hypothesis were true  Significance level: alpha, is a probability used as a criterion for rejecting the null hypothesis. Is the P-value fo
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