Small sample size and variable measurements ensure that any statistically significant difference will be huge, thus providing Kahneman- and Gladwell-bait.
– Data processing choices were made after the data were seen. In this case, two-thirds of the data were discarded because they did not fit the story. Sure, they have an explanation based on ceiling and floor effects—but what if they had found something? They would easily have been able to explain it in the context of their theory.
– Another variable (mood scale) was available. If the difference had shown up as statistically significant only after controlling for mood scale, or if there had been a statistically significant difference on mood scale alone, any of these things could’ve been reported as successful demonstrations of the theory.