Data Analysis And Outcomes For The Efficiency Ratio

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Data Analysis and Outcomes A two-tail t-Test was conducted to compare the individual vendor strategies to the calculated efficiency ratio. Three critical data points are used as a mean of testing the validity of the hypothesis: the t-Stat, critical value, and p-value. In the data if the t-Stat value is greater than the critical two-tail value then the null hypothesis (Ho) is rejected in favor of the alternate hypothesis (H1). In the first data point, comparing single-vendor strategies to best-of-suite, the t-Stat was -1.46 and the critical value was 2.26; therefore, because the t-Stat is smaller than the critical value the null hypothesis cannot be rejected and the data supports H0 that single-vendor strategies do not increase hospital efficiency more than best-of-suite. The same holds true when single-vendor is compared to best-of-breed; the data shows a t-Stat of 0.098 and a critical value of 2.11. A second way to analyze the data is to compare the p-value with the alpha. In the study, a level of significance (Alpha) of 5% or .05 was used. According to Ren (2009), the 5% represents the probability the researchers are willing to reject the null hypothesis given that the null hypothesis is true. In other words, the lower the alpha value is, the lower the risk of making a type I error, which is when the researcher fails to reject the null hypothesis when it is false. When comparing single-vendor to best-of-suite and best-of-breed, the p-value for the two-tail test was 0.17
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