Hypothesis testing - Revision history

Ciskowskid: /* Hypothesis Testing */

2022-05-11T17:34:21Z

‎Hypothesis Testing

2022-05-04T14:21:14Z

2022-05-04T02:11:05Z

‎Hypothesis Testing

2022-05-04T02:10:25Z

‎Hypothesis Testing

2022-05-04T02:10:02Z

2022-05-04T02:08:01Z

‎Hypothesis Testing

2022-05-04T02:07:29Z

‎Hypothesis Testing

2022-05-04T02:06:09Z

‎Hypothesis Testing

2022-05-04T02:05:22Z

‎Hypothesis Testing

2022-05-04T02:04:50Z

‎Hypothesis Testing

← Older revision		Revision as of 17:34, 11 May 2022
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	''contributed by Katie Ciskowski''		''contributed by Katie Ciskowski''
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		+	==Test Statistic Formulas==
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		+	[[File:Test_Statistic_Formulas.PNG]]
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		+	''contributed by David Ciskowski''

	== ==		== ==

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 Reference:
-Daren, S. S., & Tabor, J. (2020). Updated version of the practice of Statistics (Teachers Edition) (Sixth Edition). W H FREEMAN & CO LTD.
+Daren, S. S., & Tabor, J. (2020). ''Updated version of the practice of Statistics (Teachers Edition)'' (Sixth Edition). W H FREEMAN & CO LTD.
 ''contributed by Katie Ciskowski''

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 *The claim about the population that we are trying to find evidence for is the '''alternative hypothesis''' (H<sub>a</sub>).
 **A '''one-sided''' alternative hypothesis has the form H<sub>a</sub>: parameter < null value or H<sub>a</sub>: parameter > null value.
-**A '''two-sided''' alternative hypothesis has the form H<sub>a</sub>: parameter ≠null value.
+**A '''two-sided''' alternative hypothesis has the form H<sub>a</sub>: parameter ≠ null value.
 *Often, H<sub>0</sub> is a statement of no change or no difference. The alternative hypothesis states what we hope or suspect is true.
 *The '''P-value''' of a test is the probability of getting evidence for the alternative hypothesis H<sub>a</sub> that is as strong or stronger than the observed evidence when the null hypothesis H<sub>0</sub> is true.

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 *Small P-values are evidence against the null hypothesis and for the alternative hypothesis because they say that the observed result is unlikely to occur when H<sub>0</sub> is true. To determine if a P-value should be considered small, we compare it to the '''significance level α'''.
 *We make a conclusion in a hypothesis test based on the P-value.
-**If P-value<α: Reject H<sub>0</sub> and conclude there is convincing evidence for H<sub>a</sub> (in context).
+**If P-value < α: Reject H<sub>0</sub> and conclude there is convincing evidence for H<sub>a</sub> (in context).
-**If P-value>α: Fail to reject H<sub>0</sub> and conclude there is no convincing evidence for H<sub>a</sub> (in context).
+**If P-value > α: Fail to reject H<sub>0</sub> and conclude there is no convincing evidence for H<sub>a</sub> (in context).
 Reference:

@@ Line 13: / Line 13: @@
 *Small P-values are evidence against the null hypothesis and for the alternative hypothesis because they say that the observed result is unlikely to occur when H<sub>0</sub> is true. To determine if a P-value should be considered small, we compare it to the '''significance level α'''.
 *We make a conclusion in a hypothesis test based on the P-value.
-**If P-value < α: Reject H<sub>0</sub> and conclude there is convincing evidence for H<sub>a</sub> (in context).
+**If P-value<α: Reject H<sub>0</sub> and conclude there is convincing evidence for H<sub>a</sub> (in context).
-**If P-value > α: Fail to reject H<sub>0</sub> and conclude there is no convincing evidence for H<sub>a</sub> (in context).
+**If P-value>α: Fail to reject H<sub>0</sub> and conclude there is no convincing evidence for H<sub>a</sub> (in context).
 Reference:

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 '''Hypothesis Testing: The Basics'''
-*The claim that we weigh evidence "against" in a hypothesis test is called the '''null hypothesis''' (H<sub>0</sub>). The null hypothesis has the form H<sub>0</sub>: parameter = null value.
+*The claim that we weigh evidence ''against'' in a hypothesis test is called the '''null hypothesis''' (H<sub>0</sub>). The null hypothesis has the form H<sub>0</sub>: parameter = null value.
 *The claim about the population that we are trying to find evidence for is the '''alternative hypothesis''' (H<sub>a</sub>).
 **A '''one-sided''' alternative hypothesis has the form H<sub>a</sub>: parameter < null value or H<sub>a</sub>: parameter > null value.
-**A '''two-sided''' alternative hypothesis has the form H<sub>a: parameter ≠null value.
+**A '''two-sided''' alternative hypothesis has the form H<sub>a</sub>: parameter ≠null value.
 *Often, H<sub>0</sub> is a statement of no change or no difference. The alternative hypothesis states what we hope or suspect is true.
 *The '''P-value''' of a test is the probability of getting evidence for the alternative hypothesis H<sub>a that is as strong or stronger than the observed evidence when the null hypothesis H<sub>0</sub> is true.