When do candidate models represent distinct scientific hypotheses?

How should uncertainty be interpreted when multiple hypotheses remain plausible?

What is the statistical cost of favoring one hypothesis too early?

Which design features most strongly determine whether competing explanations can actually be distinguished?

Suppose two mechanisms could explain observed variation in species abundance: habitat quality and dispersal ability/limitation. You have observational data from 50 sites sampled over 5 years.

What would a design look like that could discriminate between these mechanisms?

How would you translate competing hypotheses into candidate models?

Would model comparison alone be persuasive? Why or why not?

What might you do differently in your own research after reading these papers?