p-values are sorta-abstract concepts.

Basically, the p-value (p for probability) is the likelihood of obtaining a value

**as** or

**more** extreme than the data you have, assuming that the null hypothesis (

) for whatever you're investigating is true (usually that nothing has changed between two variables, or that there is no association between two variables). The opposite of the null hypothesis is the "alternative hypothesis", or

.

To use p-values, we need a significance level (usually designated

). In statistics, a common significance level is 95%, or

.

If we use this 95% confidence/significance level, then we can say the following:

- If p-value is less than 0.05, reject in favour of (often suggesting there is a significant relationship between two variables etc)
- If p-value is greater than 0.05, DO NOT reject (suggesting that there is no association between two variables etc)

We obtain p-values from collected/observed data by performing statistical tests called hypothesis tests and confidence intervals. I don't know if you cover these in psych, but they're very useful. N.B. there are many different types of hypothesis test based on the types of variables (categorical/numerical) and the situation the data is collected from.