Not taking account of multiplicity.

Ideally you should plan exactly how you're going to analyse the data before the experiment but in reality students muddle through a bit.

analyzing data in multiple ways is a big no-no if you're just hunting for that elusive 0.05 P value to get it published.

It's stupid and causes statisticians to tear their hair out(both the arbitrary requirement a lot of journals set and the bad stats by researcher) but it's the reality in a lot of research.

Doing that can be compensated for as long as you keep track of what you tried and make that data available.

It's even worse because often people, including experienced professors, delude themselves with bad stats and waste time and money chasing statistical phantoms because they went significance mining.