Anyway, from what I remember about that process, there was a clear study design and articulation of a hypothesis--a prediction about what I expected to happen in the experiment. Years later, I would learn more about hypothesis testing in undergraduate and graduate statistical courses on my way to a social psychology PhD. For that degree, Null Hypothesis Significance Testing (NHST) would be my go-to method of inferential statistics.
In NHST, I have come to an unhealthy worship of p-values--the statistic expressing the probability of the data showing the observed relationship between variables X and Y, if the null hypothesis (of no relationship) were true. If p < .05 rejoice! If p < .10 claim emerging trends/marginal significance and be cautiously optimistic. If p > .10 find another profession. By NHST standards, an experiment fails or succeeds based solely on this one statistic.
When the Association of Psychological Science proposed using an alternative statistical approach--something called the New Statistics (actually not new, been around for decades)--I was intrigued about the possibility of living an academic life beyond the tyranny of p < .05.








