Examining Statistical Procedures And The Justification Of...

Rosnow and Rosenthal (1989) examine statistical procedures and the justification of knowledge in regards to psychological science. Psychology researchers, similar to researchers in other fields, think in inventive ways, resembling hunches and intuitions. Because of this, often progress is the result of guesses and hunches. For a long time, and at times a detriment to us as psychological scientists, we have looked at evidence as a dichotomous significance decision. In other words, significance is viewed as anti-null or pro-null depending on the value of p. A p value less than .05 can mean success, yet a p value greater than .05 can mean failure, especially when we consider publications typically disregard non-significant results for publication. While there are very valid reasons for using that key number of .05 to determine significance, we could use .06 just as well. This is evident in other scientific fields. When looking at significant and non-significant differences, values can vary between extremes due to no sharp line between the differences. The importance of a power analysis was noted, showing that working with low power and utilizing a particular sample size can impact the p value. In an example, one scientist has a larger sample size, and with this large sample size proves method A of leadership style is better than method B. The scientist that invented method B tries to replicate the original results but uses a much smaller sample size, which then will resultShow MoreRelatedYouth Outcomes : An Analysis Of Intervention And Control Group Children1105 Words   |  5 Pagesposttest data, Mendelson et al. (2010) determined that there was significance between the results those participants who participated in the intervention compared to those who were in the control group on the Involuntary Engagement Coping scale. 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Slavin goes on to explain that best-evidence synthesis includes the application of rational, systematic methods of selecting studies to be included and use of effect size (rather than statistical significance alone), as a common metric for treatment effects (p. 6). 2. In effect, Slavin merged the traditional research review with the meta-analytic approach. First, Slavin advanced the ―Principle of Best Evidenceâ€â€" which requires that the best