Test of Significance | Test Statistic | Components of Test of Significance

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Statistics with Musharaf-Ur-Rehman

Statistics with Musharaf-Ur-Rehman

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Test Statistic
A test statistic is a numerical value calculated from sample data during hypothesis testing. It is used to determine whether to reject the null hypothesis. The test statistic measures how far the sample statistic is from the hypothesized parameter under , often in terms of standard errors.
#teststatistic
#testofsignificance
#componentsoftestofsignificance
Types of Test Statistics:
1. z-test: Used for large samples or known population standard deviation.
2. t-test: Used for small samples or unknown population standard deviation.
3. χ²-test: Used for categorical data (e.g., goodness-of-fit or independence tests).
4. F-test: Used to compare variances or test models in ANOVA.
Test of Significance
A test of significance is a statistical method used to assess whether the observed data provide enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
Steps in a Test of Significance:
1. State the Hypotheses
2. Choose the Significance Level
3. Calculate the Test Statistic
4. Determine the Critical Value or p-value:
5. Decision:
In this Chapter We Discuss in Detail...
#nullhypothesis #whywedontuselevelofsignificancezero
#alternativehypothesis
#simplehypthesis #statisticalinference
#compositehypthesis #associativehypothesis
#typeoneerror #typetwoerror
#levelofsignificance #logicalhypothesis
#levelofconfidence #directionalhypothesis
#teststatistic #testingprocedure
#onetailedtest #twotailedtest #rejectionregion
#criticalregion #pvalue #occurve #powercurve #testingofhypothesis #relationbetweenoccurveandpowercurve
#testingaboutsinglemean
#ztest #ttest #ftest #chisquaretest #testinhaboitdoublemean
#testingaboutproportion
#testingaboutvariance #testingaboutdoubleproportion
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