Power analysis helps estimate the required sample size for a study.
Most of the time I conduct a power analysis for RCT designs or QED designs, using the PowerUP! software. The program is an Excel file downloadable from:
This probably is the more recent version with a lot of additions.
Occasionally I conduct a power analysis for a survey design and address a question of how many people we need to reach to achieve a sufficient statistical power. I usually use Excel for this when the item of interest is a binary variable.
What Works Clearinghouse considers an effect size of .25 (or greater) "substantively important." See p. 26, table footnote at https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v3_0_standards_handbook.pdf
J. Cohen (1988) has an idea of .2 small effect, .5 medium effect, .8 large effect.
Cohen, J. Statistical power for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum (1988). See page 5 of http://www.wmich.edu/evalphd/wp-content/uploads/2010/05/Effect_Size_Substantive_Interpretation_Guidelines.pdf .
- Optimal Design https://sites.google.com/site/optimaldesignsoftware/home
Power analysis for survey analysis (and a lot of other study settings).
- GPower http://www.gpower.hhu.de/