level of significance

• P value- ranges from 0-1, p>0.5 reject the null, p</.05 is significant
• the significance level is the probability of rejecting the null hypothesis when it is true, for example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference, lower significance levels indicate that you require strong evidence before rejecting the null hypothesis.
• can compare your p value to the significance level, if the p value is less than the significance level
• type 1 error- when researchers accept the alternate hypothesis and reject the null (in error)
• Type 2 error- researcher wrongfully accepts the null hypothesis and rejects the alternate hypothesis
• type 2- the data collected has not passed the significance level but really the participants not acting as expected is due to chance variation hiding the causal relationship between IV and DV
• Type 1- the data collected has passed the level of significance but really the findings were due to chance variation. Using P=<0.05 means this will happen in around 1 to 20 studies