In statistical tests there are 2 main types of Errors, Type I
and Type II. Type 1 errors occur when you reject a null hypothesis
that is actually true and is thus refereed to as a false positive.
Type II errors are essentially the opposite, accepting a null
hypothesis that is false, and is often called a false negative. You
can reduce the risk of a type I error by lowering the value of P
that you're significance test must return to reject the null, but
doing so will increase the chance of a type II error. The only way
to reduce both is to increase the entire sample size.
Alternatively, in some cases, it may also be possible to lower the
standard deviation of the experiment, which would also decrease the
risk of type I and type II errors.
