Exercise 40 Chi Squared Essay

1298 WordsMar 16, 20136 Pages
Researchers routinely choose an ◊-level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower ◊-level (e.g., 0.01)? What are some situations in which you might accept a higher level (e.g., 0.1)? An alpha level of 0.05 is arbitrary and was set as a standard by scientists. One of the key concepts in hypothesis testing is that of significance level or, the alpha level, which specifies the probability level for the evidence to be an unreasonable estimate. Unreasonable means that the estimate should not have taken its particular value unless some non-chance factor(s) had operated to alter the nature of the sample such that it was no longer representative of the population of interest. (Price, 2000)…show more content…
(2000). What Alpha Level? In I. Price, Inferential Statistics (p. Chapter 5). New England: University of New England. University of Texas-Houston Health Science Center . (2013). Hypothesis Testing . Retrieved March 21, 2013, from Biostatistics for the Clinician : http://www.uth.tmc.edu/uth_orgs/educ_dev/oser/L2_2.HTM 4DQ1 How would you explain the analysis of variance, assuming that your audience has not had a statistics class before? When examining the differences between two or more groups, you can use the analysis of variance which is known as ANOVA. This is a statistical technique that is used to compare the means or averages of more than two groups. There are three uses of ANOVA which are the one-way, the two-way and N-way Multivariate ANOVA. (Solutions, 2013) The determining factor when to use one of the “ways” is dependent upon how many “treatments” are used in the study. We use the term treatment because ANOVA originated in the 1920’s to test different treatments of fertilizers’ crop yields. ("Analysis of Variance," 2012, p. 2) Here, we will cover the one-way and the two-way ANOVA. The one-way between groups, ANOVA is used when you want to test the difference between two or more groups. This is the simplest version of ANOVA. (Crossman, 2013) This could be used for example in a study on the ages of patients on different cardiac medications. Here we are only looking at
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