Probability and significance

• If the statistical test is not significant, the null hypothesis is accepted
• The null hypothesis states there is 'no difference' or 'no correlation' between the conditions
• The statistical test determines which hypothesis (null or alternative) is 'true' and this which we accept and reject
• Probability is a measure of the likelihood that a particular event will occur, where 0 is a statistical impossibility and 1 a statistical certainty

  There are no statistical certainties in psychology but there is a significance level- the point at which the null hypothesis is accepted or rejected
• The usual level of significance is 0.05 (or 5%)

  This means there is a 5% chance that the results of a particular study sample occurred even if there was no real difference in the population (i.e. the null hypothesis is true)
• Stringent levels of significance (i.e. 1%) may be used in life or death or one-off situations
• Type I error:

  The null hypothesis is rejected and the alternative hypothesis is accepted when the null hypothesis is 'true'

  This is an optimistic error or false positive as a significant difference or correlation is found when one does not exist
• Type II error:

  The null hypothesis is accepted but, in reality, the alternative hypothesis is 'true'

  This is a pessimistic error or a false negative
• What makes a type I error more likely?

  If the significance level is too lenient (too high e.g. 0.1 or 10%)
• What makes a type II error more likely?

  If the significance level is too stringent (too low, e.g. 0.01 or 1%) as potentially significant values may be missed