*Saturday, November 15th, 2014*

Again, wikipedia sums it up

In statistical significance testing, the p-value is the probability of obtaining a test statistic result at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. A researcher will often “reject the null hypothesis” when the p-value turns out to be less than a predetermined significance level, often 0.05 or 0.01. **Such a result indicates that the observed result would be highly unlikely under the null hypothesis.** Many common statistical tests, such as chi-squared tests or Student’s t-test, produce test statistics which can be interpreted using p-values.

More examples of calculating the p value

- Two sample t-test
- paired t test
- One sample t test
- chi square test statistic
- Derivation of the linear least squares

- Connecting to Google Analytics – Brighton PHP October 2013 on
- udacity Introduction to statistics on
- udacity Introduction to statistics on
- jasonbailey.net is up on
- jasonbailey.net is up on

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