Inferential testing is used to see whether a study's results are statistically significant
The threshold where results are considered statistically significant is usually <0.05, which means there is a less than 5% chance the observed effect is due to chance
The lower the p (probability) value, the more statistically significant results are
There are two types of errors when interpreting statistical significance:
Type one (false positive)
Type two (false negative)
A type 1 error is when researchers conclude an effect is real (they reject the null hypothesis) when it is not
A type 2 error (false negative) is when researchers conclude there is no effect and accept the null hypothesis when the effect is real
Type 1 errors are usually due to a probability threshold that is too high
Type 2 errors are usually due to a probability threshold that is too low
