Statistical Tricks: Navigating Research Credibility Amidst the ocean of research findings, how can we untangle statistical significance from potential deceit? This week we explore the proposed rules of thumb that unveil the subtle art of identifying potentially flawed research on the path to truth. Our goal is to get to the point where critical thinking skills guard the gates against statistical manipulation or negligence. The following are the proposed five rules of thumb for judging when a statistically significant research finding is probably untrue: 1) The sample size (or the number of observations) is small; 2) The size of the effect is small; 3) Tests of the data are numerous and exploratory, versus targeted and confirmatory; 4) The number of researcher degrees of freedom (discussed below) are high; 5) External incentives to get significant results are high (as when funding of future research depends on publication, and publication depends on statistically significant findings); and 6) A field is "hot," that is, when a larger number of research teams focus on an issue and compete to produce impressive results Your Task: . Discuss how to identify at least 3 of the rules of thumb above. Why are these rules of statistical significance important to consider? How could the research be changed to be made more accurate?