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BSCI 1511L Statistics Manual: 4 Summary of statistical tests

Introduction to Biological Sciences lab, second semester

Summary of tests

Criteria for choosing the correct test:

t test of means:

–values are continuous

–there is no reason why a particular value in one group would be related to a particular value in the other group

–comparing two group means


paired t test:

–values are continuous

–there is a connection between particular values in one group and corresponding values in the other group

–comparing differences between pairs of values in the two groups


linear regression:

–values are continuous

–values representing the experimental factor vary continuously rather than fall into discrete categories

–determining whether the line of best fit has a slope greater than zero


ANOVA (analysis of variance, described in Section 3):

–values are continuous

–comparison of more than two group means

–may analyze more than one experimental factor

–determining whether means of several groups are different


chi squared contingency (described in Section 2):

–values are counts

–comparing whether states of one factor are associated with states of another factor


chi squared goodness of fit (described in Section 1):

–values are counts

–comparing observed and expected frequencies



Appropriate null hypotheses for the statistical tests discussed .  Note: these are stated in general terms.  You should state them in more specific terms based on the details of the particular design of the experiment.


t test of means:

–The true means of the two groups are the same (i.e. differences in sample means are due only to chance).


paired t test:

–The average difference between pairs of blocked data is zero (i.e. pairs of blocked data deviate randomly).


linear regression:

–The slope of the best fit line through the data is zero (i.e. deviations from a best-fit line having slope of zero are random).


ANOVA (analysis of variance):

–There is no difference among the means of several groups.


chi squared contingency:

–There is no association between the state of one factor and the state of another factor (i.e. the frequencies are the same as those that would be predicted if they were independent)


chi squared goodness of fit:

–The frequencies observed in the categories are the same as the expected frequencies