Probability and Significance Testing

• null hypothesis (H0)


  predicts there will be no effect between variables
• alternative hypothesis (H1)


  predicts an effect between the variables (either directional or non-directional)
• significance


  the researcher can state that the relationship between the variables is due to more than just chance- they can accept the alternative hypothesis and reject the null hypothesis
• inferential statistics


  a way of determining whether differences or relationships between variables are statistically significant or have occurred by chance
• probability in the context of statistical testing


  the likelihood that the data obtained is due to chance (rather than manipulation of the IV)
• usual level of significance used in psychology


  p_<0.05 or 5%
• when psychologists might adopt a more stringent significance level of 0.01


  -when there is a human cost eg. considering the effects of a new drug

  -when the results are theoretically important
• type I error


  rejection of the null hypothesis when it should have been accepted (false positive)
• type II error


  wrongly accepting the null hypothesis when it should have been rejected (false negative)
• when researchers are more likely to make a type I error


  when the significance level is too lenient (0.1 or 10%)
• when researchers are more likely to make a type II error


  when the significance level is too strict (0.01 or 1%)