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I am assessing power analyses in published papers (for reasons), and there are some quite common errors. Here follows a thread of these issues. The goal is not to make fun of folks, who I am sure are all trying to do the right thing, but to point out things to avoid doing. 1/11
It’s not uncommon to report a power analysis for a one-sided test and then use two tailed tests. This will overestimate the power of your statistical tests. Please don’t do this 
. 2/11
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It's also not uncommon for people to report power analyses for a different analytic strategy than is used to analyse the data. If you’re planning your study using a power analysis, you should make sure you plan for the analysis you will conduct. 3/11
Often important parameters are not reported – e.g., people do not always state the effect size or goal power that they used in a power calculation. You should report all of the values used in a power calculation. 4/11
Equally, it’s probably better to directly state the effect size used in a power analysis rather than making people go to another paper to find it. 5/11
Sometimes people don’t state which type of effect size they used – without knowing which was used it’s hard to know whether the chosen value was reasonable (Cohen’s f was probably used here, but I only know that because I have hung out with G*Power a lot). 6/11
People often also just use Cohen’s effect size benchmarks for a power analysis, which seem extremely unlikely to be the best possible estimate of the effect size under study that you could develop. 7/11
People sometimes worry about ‘overpowered’ studies, but as long as the effect sizes are considered when interpreting results, this is not really a legitimate concern (barring wasting participants or your own time and resources, involving participants in risky research, etc.) 8/11
“we practically achieved our goal sample size” means "we did not achieve our goal"-to avoid ambiguous language you could report a sensitivity analysis to show that you almost reached your goal level of power at your effect size estimate despite the missing participants 9/11
Post hoc power analyses using observed effect sizes to dismiss non-significant findings are also still quite common despite the circular reasoning, better to just report confidence intervals around parameter estimates. 10/11
In case anyone wants a good example, here’s a good one, it’s clear, reasonably concise, gives credit to the authors of the package used to run the power analysis, tells us where the effect size came from, and accounts for publication bias in their effect size estimate. 11/11
