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you piched the answer you picked. On some questom wu will be aslud to loentity the steps you tock to gnt answer you got. On sonve questions yosr'll be asked to xplain or desarilm a concept. You should NOT google these questions and copy and paste an answor fronms thyreook You should eaplain thene amwars in your You can shways email me it you do not undrestand AoCanas h rumoy for any mason own words Question 20 3 pts Think about the previous question f I have a continuous dependent variable and a nominal independent varñlabie c um a ttest o an ANOVA to analyze my variables. If I only have tiwe levets in my independlent variabw (very rich rich, average, poor very poorl. I can sun an ANOVA or I can run multiple1 tests Why is it better to run one ANOVA to analyze this data rather than miltiple itests? Aiswer this cuestion in non-reseanchy terne

Answer 1

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Every time you conduct a t-test there is a chance that you will make a Type I error. This error is usually 5%. By running two t-tests on the same data you will have increased your chance of "making a mistake" to 10%. The formula for determining the new error rate for multiple t-tests is not as simple as multiplying 5% by the number of tests. However, if you are only making a few multiple comparisons, the results are very similar if you do. As such, three t-tests would be 15% (actually, 14.3%) and so on. These are unacceptable errors. An ANOVA controls for these errors so that the Type I error remains at 5% and you can be more confident that any statistically significant result you find is not just running lots of tests. See our guide on hypothesis testing for more information on Type I errors.