Decisions You Can Take
When testing, there are two decisions that can be made: to accept the null hypothesis or to reject the null hypothesis.When testing, there are two decisions that can be made: to accept the null hypothesis or to reject the null hypothesis.
To accept the null means that there isn’t enough data to support
the change or the
innovation brought by the alternative. To reject the null means that there is enough statistical evidence that the status-quo is not representative of the truth.
Given a two-tailed test:
Graphically, the tails of the distribution show when we reject the null hypothesis (‘rejection region’).
Everything which remains in the middle is the ‘acceptance region’.
The rationale is: if the observed statistic is too far away from 0 (depending on the significance level),
we reject the null. Otherwise, we accept it
Different ways of reporting
the result:
Accept
At x% significance, we accept the null
hypothesis
At
x% significance, A is not
significantly different from B
At x% significance, there is not
enough
statistical evidence that… At x% significance, we cannot reject
the null hypothesis
Reject
At x% significance, we reject the null
hypothesis
At x% significance, A is significantly different from B
At x% significance, there is enough statistical evidence… At
x% significance, we cannot say that
*restate the null*
Level of Significance and Types of Tests
