Probability, significance, type 1 and type 2 errors Flashcards
What is a level of statistical significance?
The level at which the decision is made to reject the null hypothesis in favour of the experimental hypothesis.
It states how sure we can be that the IV is having an effect on the DV and this is not due to chance.
Significance levels and probability
By looking at the results from our experiment from both conditions we would look to see if there is a real difference between the two sets of data and how certain of that are we. If a statistical test says there is no difference you would accept the null hypothesis but if there is a high enough possibility of a real difference then we would accept the experimental hypothesis and reject the null hypothesis as the result is significant.
Probability is numerical measure of whether a result is due to chance or if there is real difference. If there is a real difference we can say a result is significant.
Significance levels used in psychology
the standard significance level is expressed as p<0.05, which is the 5% level. This is due to the fact that it is not too strict or two lenient, but is a middle fair value for significance. It is also the best value of minimising the chance of a Type 1 or Type 2 error.
The āpā stands for probability and 00.5/5% stands for the significance level that has been chosen. This means there is 95% certainty that our results are significant.
Stricter (1%) or more lenient (10%) significance levels can be used depending on the experiment and the importance of the data.
What are Type 1 and Type 2 errors?
Type 1 error- This is when we might reject the null hypothesis and accept the experimental hypothesis but this wad due to chance, a false positive most likely due to a lenient significance level.
Type 2 error- This is when we accept the null hypothesis when the results are actually statistically significant and not caused by chance, a false negative often caused by a overly harsh significance level.
