# Interpreting p-value significance

If you have performed a statistical test using the standard 95% confidence threshold then a p-value of < 0.05 will indicate significance.

I was reading a blog post about typesetting some correlation data using the R library xtable. The author uses a series of ifelse statements to replace p-values with stars to indicate whether the result was less than 0.001, 0.01, or 0.05. I was wondering if anyone could comment on the validity of this?

If you perform a statistical test at a 95% confidence level and you get a p-value of < 0.01 does that mean that you could say that the result is significant to the 99% level. Or can you only assume a stronger confidence level if you explicitly performed the test with that criteria?

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## 1 Answer

Calculation of a p-value does not require any CI to be mentioned. If you have the z-score, you can calculate the p-value by integration over the normal distribution from -inf to the z-score. Or, you could look it up in z-tables.

That's why if you perform a statistical test at a 95% confidence level and you get a p-value of < 0.01 that does mean you could say that the result is significant to the 99% level too.

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Of course! I was forgetting when you perform a test you get back the z-score and then compare it with the value in table. It's been a long time since I've actually done that manually. – Alastair Nov 27 '12 at 17:30
It's actually tough to do manually if we get statistic that follow other distributions. Calculating from z-score is easy though. – Blain Waan Nov 27 '12 at 17:36