statistical tests

chi-square: compare observed data vs theoretical data from a hypothesis

T test: determine if there is a significant difference between mean values of two groups

analysis of variance (ANOVA): compares the means of two or more sets of data by calculating how widely individual values in each data set vary. If they vary greatly from the mean, the
variance is large, and vice versa. When applied to only two data sets, ANOVA will give the same result as a t-test. ANOVA is a powerful statistical test because it allows you to test for each factor while controlling for others and to detect whether one variable affects another. For example, if you were compar -ing the activity of a particular enzyme in mainland and island tortoises, you might want to determine whether sex affects enzyme activity, so you could also separate the data sets by sex.

correlation analysis: find correlation between two variables

regression analysis: evaluate the scatter of data points around a “line of best fit”

    R²- evaluates the scatter of the data points around a fitted line. higher the R², the better the line fits data

p> 0.05 - not statistically significant (greater than a 1 in 20 chance of being wrong, i.e, incorrect rejection of the null hypothesis)

p<0.05 - statistically significant

p <0.01 - stat. sig

p<0.001 - stat. sig