alternate hypothesis - states there will be a difference/effect
Null hypothesis - there will be no difference/ effect
A statistical test allows us to decide which of these hypothesis is true and whether we accept or reject the nill hypothesis
Rejecting the null hypothesis means the study is good by using a statistical test
Significance
Statistical test is used to test whether results from a study is significant change in IV and not by chance/fluke
Probability
Generally accepted level of significance is 0.05% this is the level where the alternate hypothesis is accepted or the null hypothesis is rejected
0.05% means that there is a less than 5% probability the results occured by chance
- So if results after conducting a statistical test is significant there is a 95% probability the results occurred due to a manipulation of the IV
Choosing the wrong significance level
Lead to error
Type 1 error -
If significance level is to lenient/ to big eg 10% significance level 0.1 it can result in rejecting the null hypothesis when we should accept it. Says the results are significant when they are not
Type 2 error is when the significance level is to small eg results in accepting null hypothesis when we should have accepted the alternate hypothesis saying results are insignificant even though they are significant
Establishing if results are significant or not :
Establishing whether results are significant or not the critical values table is given
- N value is the number of participants in the study
- Calculated value comes from the statistical test depends on the test usually S
- Significance level is always 0.05% unless told otherwise
- Is the test one tailed or two tailed (directional or non directional)
- Critical value is found from the table
Are my results significant?
The calculated value of S must be equal to or less than the critical value in order to be significant if yes then results are significant
