probability and significance Flashcards
what is significance and what happens when results are significant/not significant?
Significance is a statistical term which lets us know how sure we are about a correlation or difference existing. If significant, we reject the null hypothesis and accept the alternative hypothesis. The difference between these two types of hypotheses is the null (H0) one states ‘there is no difference or correlation between the conditions’ whilst the alternative (H1) one states ‘there is a difference between the conditions’.
what is probability?
Probability is a calculation of how likely it is for an event to happen - 0= statistical impossibility and 1= statistical certainty. The usual level of significant in psychology is 0.05. Therefore the p value is usually equal to or less than 0.05 (5%) which means that the probability of the difference in the study’s findings being due to chance is 5% or less so researchers have a 95% confidence level in their results. If there is any risk attached to the research like a ‘human cost’ e.g. with clinical drug trials then the p values is set at 0.01 (1%) instead.
why are statistical tests used and how are they calculated?
Statistical tests are used to determine whether a significant difference or correlation exists. This is discovered using the calculated value (the result obtained from the statistical test) and the critical value (the numerical boundary that stands between accepting or rejecting the null hypothesis when a hypothesis is being tested). The critical value is worked out from a table of probability values and depends on various factors: whether it was a one or two tailed test, the P value and either the N value or the degrees of freedom value.
what is the rule of R?
Rule of R - If there is an R in the name of the statistical test the calculated value has to be greater or equal to the critical value for the result to be significant. If this is the case then the null hypothesis can be rejected and the alternative hypothesis is supported. If there is no R in the test’s name then the calculated value has to be less than or equal to the critical value for it to be significant.
what is a type I error?
Type I (optimistic) error is the incorrect rejection of a null hypothesis which is actually true. Researchers claim to have found a significant difference when there actually isn’t any (a false positive).
what is a type II error?
Type II (pessimistic) error is the failure to reject the null hypothesis that is false. Researchers claim that there is no significant difference when there actually is one (a false negative).
what are the three criterias of choosing which critical value to use?
-is it one tailed or two tailed?- one tailed is used if the hypothesis was directional and two tailed if hypethesis was non directional
-the number of participants in the study- usually appears as the N value on the table
-the level of significance (or p value)- 0.05 is the standard level in psychological research.
