Statistical tests (intro to biostats lecture) Flashcards
Student t-test
Interval
2 groups
Independent
Compares the means of all groups (along with intra and inter-group variations) against a dependent variable
(Pearson’s) Chi square (include its 2 assumptions)
Nominal
2 Groups
Independent
Compares group proportions and if they are different from that expected by chance
(usual chi-square data distribution and no cell count less than 5)
Fisher’s exact test
Nominal
2 groups OR more than 2 groups
Independent
Groups have an EXPECTED cell count of <5
(Bonferroni to adjust the p value for the # of groups being compared IF there are more than 2 groups being compared)
Chi square test of independence
Nominal
More than 2 Groups
Independent
(Bonferroni to adjust the p value for the # of groups being compared)
(usual chi-square data distribution and no cell count less than 5)
Bonferroni test of inequality (include the 3 tests run prior to the use of the bonferroni test)
Nominal
More than 2 groups
Independent OR related data
adjusts the p value for the # of groups being compared
(Chi squared of independence, Fisher’s exact, and Cochran are all tests that require the bonferroni as a “post hoc” test if they yield statistically significant findings [p value < 0.05])
McNemar test
Nominal
2 groups
Related
Cochran Q test
Nominal
More than 2 groups
Related data
Same principle/assumptions as chi squared, but it factors in related/paired data
(Bonferroni to adjust the p value for the # of groups being compared)
Mann-Whitney Rank Sum
Ordinal
2 groups
Independent data
Compares the median values between groups
(also used for interval data that does not meet parametric requirements)
Wilcoxson Signed Ranke
Ordinal
2 groups
Related data
Compares the median values between groups
Kruskal-Wallis Test (include what it needs if its value is statistically significant)
Ordinal
more than 2 groups
Independent data
Compares the median values between groups
(Student-Newman-Keul, Dunnett, or Dunn post hoc tests must be run to determine where there differences are when there are more than 2 groups)
(also used for interval data that does not meet parametric requirements)
Friedman Test (include what it needs if its value is statistically significant)
Ordinal
More than 2 groups
Related data
(Student-Newman-Keul, Dunnett, or Dunn post hoc tests must be run to determine where there differences are when there are more than 2 groups)
Levene test of equal variances
determines how “normally distributed” and the level of “equal variances” that are occurring in the data set
“it asks if the groups are equal”
paired t test
Interval
2 groups
Related data
Compares the mean values between groups that are related
ANOVA (include what it may need and why)
Interval
More than 2 groups
Independent data
Compares the means of all groups (along with intra and inter-group variations) against a dependent variable
(if this group comparison yields a statistically significant value, then a post hoc test must be conducted)(Bonferroni, Tukey, Scheffe, Dunn, Dunnet, Student-Newman-Keul)
ANCOVA (include what it may need and why)
Interval
More than 2 groups
Independent data
Confounders present
Compares the means of all groups (along with intra and inter-group variations) against a dependent variable while also controlling for the co-variance of confounders
(if this group comparison yields a statistically significant value, then a post hoc test must be conducted)(Bonferroni, Tukey, Scheffe, Dunn, Dunnet, Student-Newman-Keul)
Repeated measures ANOVA
Interval
More than 2 groups
Related data
Compares the means of all groups (along with intra and inter-group variations) of RELATED data against a dependent variable
(if this group comparison yields a statistically significant value, then a post hoc test must be conducted)(Bonferroni, Tukey, Scheffe, Dunn, Dunnet, Student-Newman-Keul)
Repeated measures ANCOVA
Interval
More than 2 groups
Related data
Confounders present
Compares the means of all groups (along with intra and inter-group variations) against a dependent variable while also controlling for the co-variance of confounders
(if this group comparison yields a statistically significant value, then a post hoc test must be conducted)(Bonferroni, Tukey, Scheffe, Dunn, Dunnet, Student-Newman-Keul)
Cox-Proportional Hazards test
Ordinal
Proportion with event (survival)
Compares the number of ordinal data events over time
Logistic regression
Nominal
Prediction or Association
(Predicts the outcome [dependent variable])
(you are able to calculate an OR for a measure of association)
Linear Regression
Interval
Prediction or Association
(Predicts the outcome [dependent variable])
(you are able to calculate an OR for a measure of association)
Multinominal Logistic Regression
Ordinal
Prediction or Association
(Predicts the outcome [dependent variable])
(you are able to calculate an OR for a measure of association)
Kaplan-Meier test
Interval
Proportion with event (survival)
Time itself is being evaluated
What can be represented by a kaplan-meier curve?
all survival tests
Log-Rank, Cox-Proportional hazards, and kaplan-meier test
Contingency Coefficient
Nominal
Correlation is desired
(partial correlation if you need to control for confounding)
