Parametric Tests of Difference Flashcards
reasons for choosing a related t test
4
the hypothesis states a difference between two sets of data
the two sets of data are pairs of scores from one person (i.e. related data)
the data is interval because there are equal intervals when counting frequency
the data fits the criteria for a parametric test — it is interval, the population is assumed to have a normal distribution and the variances of the samples are the same because they come from the same participants
how to conduct a related t test
6
1) state hypothesis — either an alternative hypothesis (directional or nondirectional) or a null hypothesis
2) collect and place raw data in a table — there should be two items for each participant (one from each participant)
3) find calculated value of T
4) identify if the result is in the right direction in relation to your hypothesis (by looking at the means for each group)
5) find the critical value of T — using significance level 5%, identify the kind of hypothesis (one tailed test if directional and vice versa), identify the df value which is total number of scores for each group minus 1 (ignoring zero values), locate the row in the statistical table that begins with your df level value, the number in the box is the critical value of T
6) report the conclusion — if the calculated value is equal to or greater than the critical value, the result is significant
reasons for choosing an unrelated t test
4
the hypothesis states a difference between two sets of data
the two sets of data are pairs of scores from separate groups of participants (i.e. unrelated data)
the data is interval because there are equal intervals when counting frequency
the data fits the criteria for a parametric test — it is interval, the population is assumed to have a normal distribution and the variances of the samples are assume to be the same because participants were randomly assigned to conditions
how to conduct an unrelated t test
6
1) state hypothesis — either an alternative hypothesis (directional or nondirectional) or a null hypothesis
2) collect and place raw data in a table
3) find calculated value of T
4) identify if the result is in the right direction in relation to your hypothesis (by looking at the means for each group)
5) find the critical value of T — using significance level 5%, identify the kind of hypothesis (one tailed test if directional and vice versa), identify the df value which is NA + NB minus 2, locate the row in the statistical table that begins with your df level value, the number in the box is the critical value of T
6) report the conclusion — if the calculated value is equal to or greater than the critical value, the result is significant
what happens if the results are in the wrong direction?
2
if the results are in the wrong direction we can’t accept the alternative hypothesis and cannot draw any conclusion
in fact, once we spotted that the data was in the wrong direction there was no point calculating the statistic
