With the large sample size, we normally somehow interpret the coefficient significant at 10%, but in the small sample size, we normally ignore the 10% significant level. The reason is that with the large sample size, 10% significant level still makes a substantial contribution.
I am wondering is there any reference or justification about the sample sizes and significance level being noticed? For example, under 1000 observations we just focus on p-value<0.05, or over 100,000 observations, we can focus on p-value <0.1 (just my example to clarify my question).
Much appreciated.
Related Posts with How can we choose the appropriate significance level to interpret based on sample sizes?
command ifgt is unrecognisedHello All. I was running some microsimulation code and got the error "command ifgt is unrecognised".…
Trend variable in a Tobit modelHello everyone, I would like to add a trend variable in my Tobit model since I was adviced not to …
How to create a dbf/gal file from a dta file? (spatmat command)Hello everybody! If I have a gal/dbf file of a contiguity matrix (created through geoda, e.g.), in …
Saving CSV files in StataHi, I am trying to save changes in a CSV file in STATA. I have imported the file, wrote the command…
First effects vs first differenceHello all, I am running two models with exactly the same variables (a fixed effects and a first dif…
Subscribe to:
Post Comments (Atom)
0 Response to How can we choose the appropriate significance level to interpret based on sample sizes?
Post a Comment