[vharuck]

When I advise decision makers on reading statistics (in my case, state-wide health data), I urge them to focus on effect size and only use significance as a filter. Two reasons:

1. Effect size is the most important thing. The point of the study is (usually) to guide decisions. Sticking with the article's example, let's say combining both studies shows the increase is likely 0.35 standard deviations. Is the intervention still worth the cost? Is it still the best option?

2. If there's enough data (e.g., an observational study) or a good chance of omitted variables, there's going to be a "statistically significant" difference. No matter what's measured. I would bet my life's savings there's a statistically significant difference in profits of New York businesses depending on whether the owner's named Jim or Bob. A replication of the experiment with all Jim and Bob businesses in another state would also guarantee significance. So it's a coin toss whether the second study would "successfully replicate" the same direction of effect.