We found this handout to be incredibly useful as a guide and resource for non-statistical professionals to make quick decisions about statistical methods. The handout accompanies the Commonly Used Statistics in Medical Research Part I Presentation

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HOW TO INTERPRET COMMONLY USED STATISTICS IN MEDICAL RESEARCH
WHY LEARN HOW TO READ STATISTICS ?
Several studies have reported the error rate in reporting and/or interpreting statistics in the medical literature is between
30-90% (Novak et al., 2006).
Understanding basic statistical concepts will allow you to become a more critical consumer of the medical literature, and
ultimately be able to produce better research and make better clinical decisions.
THREE MAJOR CATEGORIES OF STATISTICAL TESTS
Descriptive Statistics:
These are numbers used simply to describe the sample population of the study. They do not actually test any hypotheses,
or yield any p-values.
Example descriptive statistics: Frequencies, mean, median, percentages, standard deviation.
Parametric (distribution-dependent) Statistics:
These are the most powerful type of statistics we use. Unfortunately, researchers must make sure their data meets a
number of assumptions (or rules) before these tests can be used properly. In research, you always want to use parametric
statistics if possible.
Example parametric statistics: Independent t-test, Pearson r correlation, Analysis of Variance (ANOVA)
Nonparametric Statistics:
A less-powerful group of statistical analyses that are used either when the researcher has violated the assumptions (i.e.
broke the rules) necessary to run parametric statistics or when using categorical or ordinal variables. They are also used to
test the risk or odds of someone an outcome occurring. These tests are extremely common due to the nature of many
research studies, and parametric statistics have a nonparametric counterpart that tests the same type of hypotheses. Some
research questions can only be asked non-parametrically (e.g. odds of developing cancer based on smoker/non-smoker).
Example nonparametric statistics:Mann-Whitney U (independent t-test equivalent), Odds/Risk, Survival
Analysis, Logistic Regression, Spearman Rho (Pearson r equivalent), and Kruskall-Wallis (ANOVA equivalent)
DIFFERENT TYPES OF STATISTICAL TESTS
Tests of Relationships:
These analyses look at the relationship between a set of variables. Specifically, they seek to determine how an outcome
(dependent) variable changes in response to changes in one or more predictor (independent) variables.
Example tests of relationships: Pearson r correlation, Spearman Rho correlation, multiple regression.
Tests of Group Differences:
This group of statistical tests focuses on determining the average difference between two or more groups of independent
participants. They compare the mean (average) score for each group on an outcome (dependent) variable.
Example tests of group differences: Independent t-test, between-subjects analysis of variance (ANOVA),
analysis of covariance (ANCOVA).
Tests of Repeated Measures:
A group of tests which looks at the difference in average score on an outcome (dependent) variable between two or more
time-points using the same group of participants. These tests are used to compare pretest and posttest scores, or other
changes over time.
Example tests of repeated measures: Dependent t-test, repeated-measures analysis of variance.
Prepared By: Tiffany Smith, Patrick Barlow, and Eric Heidel

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Tests of Odds / Risk:
A group of Non-Parametric tests which look at the odds or risk (risk is prospective, odds is retrospective) of an event
occurring or not occurring based on one or more predictor variables (independent). These tests involve categorical
variables as the independent variables and a dichotomous dependent variable (i.e. develops cancer, yes or no).
Example tests of odds/risk: Chi-square, Odds Ratio / Relative Risk, Logistic Regression, Survival Analysis.
COMMONLY USED STATISTICAL TESTS:
Pearson R Correlation: A statistical analysis that tests the relationship between two continuous variables.
Commonly Associated Terms:bivariate correlation, relationship, r-value, scatterplot, confidence interval,
relationship, association, direction, magnitude.
What to interpret: p-values (<.05), EFFECT SIZE (square the r-value to obtain effect size), magnitude of the
relationship (between -1.0 and 1.0), direction (positive or negative), weak |.1|-|.3|, moderate |.3|-|.5|, strong |.5|-
|1.0|
How to interpret:
There is a significant positive relationship between the two variables, where as one increases, the other
also increases.
There is a significant negative relationship between the two variables, where as one increases the other
decreases.
Non-Parametric Equivalent: Spearman Rho
Linear/Multiple Regression: A statistical analysis that tests the relationship betweenmultiple predictor variables and
onecontinuousoutcome variable.
Commonly Associated Terms:multivariate,beta weight,r2-value, relationship, model, forward regression,
backward regression, sequential/hierarchical regression,standard/simultaneous regression, statistical/stepwise
regression, confidence interval, correlation, association, direction, magnitude.
What to interpret: p-values (<.05), EFFECT SIZE (square the r-value to obtain effect size), magnitude of the
relationship beta weights: beta < 1 = protective effect/negative relationship, beta > 1 = positive relationship.
How to interpret:
Beta is positive: There is a significant positive relationship between the predictor and outcome variables,
whereas the predictor increases by 1 unit (e.g. 1lbs to 2lbs), the outcome variable also increases by (beta)
after controlling for *at least one* other covariate.
Beta is negative:There is a significant negative relationship between the two variables, whereas the
predictor increases by 1 unit (e.g. 1lbs to 2lbs), the outcome variable also decreases by (beta) after
controlling for *at least one* other covariate.
Independent T-Test: A statistical analysis that tests differences between two independent groups at one time-point.
Commonly Associated Terms:two sample t-test, student’s t-test, means, group means, standard deviations,
mean differences, case-control, group difference, confidence interval, group comparison.
What to interpret: p-values (<.05), large mean differences and small standard deviations based on your judgment
of the variables included (via literature review, clinical expertise, etc.), EFFECT SIZE
How to interpret:
There is a significant difference between the two groups where one group has a
significantly higher/lower score on the dependent variable than the other.
Non-Parametric Equivalent: Mann-Whitney U
One-Way Between Subjects Analysis of Variance (ANOVA): A statistical analysis that tests differences between two
or more independent groups at one time-point.
Commonly Associated Terms:two or more groups, means, standard deviations, confidence interval, group
differences, group comparisons, F-test, interactions, post-hoc tests (tukey HSD, bonferroni, scheffe, dunnett, etc.).
What to interpret: main effect, post-hoc, p-values (<.05), large mean differences between two or more groups and
small standard deviations based on your judgment of the variables included, EFFECT SIZE
How to interpret:
Prepared By: Tiffany Smith, Patrick Barlow, and Eric Heidel

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Main Effect – There was an overall significant difference among the groups of the independent variable
on the dependent variable, however we must figure out where that difference is through the post-hoc test
that was performed in the study.
Post-Hoc: There is a significant difference between the two groups where one (or more) group(s) has a
significantly higher/lower score on the dependent variable than the other(s).
Non-Parametric Equivalent: Kruskall-Wallis with follow-up Mann-Whitney U tests
Dependent T-Test: A statistical analysis that tests differences of one group between two time-points.
Commonly Associated Terms:pre and posttest, matched pairs, means, standard deviations, mean differences,
confidence interval, paired samples, time.
What to interpret: p-values (<.05), large mean change between two time-points and small standard deviations
based on your judgment of the variables included, EFFECT SIZE
How to interpret:
There is a significant difference between the pretest and posttest where the score on the posttest was
significantly higher/lower on the dependent variable than the pretest.
Non-Parametric Equivalent: Wilcoxon Matched Pairs
Repeated Measures ANOVA: A statistical analysis that tests differences of one group between two or more time-points.
Commonly Associated Terms:multiple time-points (e.g. pretest, posttest, follow-up; not JUST pre and post),
means, standard deviations, confidence interval, mean differences, time series, F-test, interactions, repeated
measures, post-hoc tests (Tukey HSD, Bonferroni, Scheffe, Dunnett, etc.).
What to interpret: main effect, post-hoc, p-values (<.05), large mean change between two or more time-points
and small standard deviations based on your judgment of the variables included, EFFECT SIZE, direction of
change (Do scores increase between each time-point? Do they decrease at each? Is it a mix of both?)
How to interpret:
Main Effect – There was an overall significant difference among the different time-points on the
dependent variable, however we must figure out where that difference is through the post-hoc test that
was performed in the study.
Post-Hoc: There is a significant difference between the pretest, posttest, and follow-up where scores at
one or more time-points were significantly higher/lower on the dependent variable than the other time-
point(s).
Non-Parametric Equivalent: Friedman ANOVA with follow-up Wilcoxon Matched Pairs tests
OTHER ANOVAs:
Mixed ANOVA: A statistical analysis that tests differences between two or more independent groups at two or
more time-points.
ANCOVA:A statistical analysis that tests differences between two or more independent groups at one time-point
while controlling for other variables.
Multivariate ANOVA (MANOVA):A statistical analysis that tests differences between two or more independent
groups on multiple dependent variables.
Odds Ratios / Relative Risk:A statistical analysis that tests the odds or risk of an event occurring or not occurring based
on one or more predictor variables (independent). These tests involve categorical variables as the independent variables
and a dichotomous dependent variable (i.e. develops cancer, yes or no). (A Fisher’s Exact Test is used when you have a
small sample size (n < 20) or when one of the cells of a 2x2 table has fewer than 5 observations. It is interpreted the exact
same way as a Chi Square.)
Commonly Associated Terms:unadjusted odds ratio (OR), relative risk, 2x2, chi-square, absolute
riskreduction, absolute risk, relative risk reduction, odds, confidence intervals, protective effect, likelihood,
forest plot.
What to interpret: p-values (<.05), confidence interval (should not cross over 1.0), odds ratio (<1 is a protective
effect, >1 is increased odds/risk)
How to interpret:
Prepared By: Tiffany Smith, Patrick Barlow, and Eric Heidel

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Odds Ratio < 1:For every unit increase in the independent variable, the odds of having the outcome
decrease by (OR) times.
Odds Ratio > 1:For every unit increase in the independent variable, the odds of having the outcome
increase by (OR) times.
Odds Ratio = 1or CI crosses 1.0 or p > .05:You are no more or less likely to have the outcome as a result
of the predictor variable. (this would be non-significant)
Logistic Regression:A statistical procedure that attempts to correctly predict the occurrence or non-occurrence of an
event (i.e. dichotomous DV) based on multiple predictor variables (IVs). This is considered a multivariate (multiple
variables) approach to looking at research questions dealing with odds, risks, and proportions.
Commonly Associated Terms:adjusted odds ratio(AOR), multivariate adjusted odds ratio,likelihood,
protective effect, risk, odds, 95% confidence interval, classification table, dichotomous DV, backward regression,
forward regression, standard/simultaneous regression, sequential/hierarchical regression, statistical/stepwise
regression.
What to interpret:OR (these are your measures for risk of the outcome occurring given the predictor variable), p-
value for OR, confidence intervals for OR (should not cross over 1.0, should not be overly large e.g. 1.2 – 45.5),
classification table (if it is provided).
How to interpret:
Odds Ratio < 1:For every unit increase in the independent variable, the odds of having the outcome
decrease by (OR) times after controlling for *at least one* other covariate.
Odds Ratio > 1:For every unit increase in the independent variable, the odds of having the outcome
increase by (OR) times after controlling for *at least one* other covariate.
Odds Ratio = 1 or CI crosses 1.0 or p > .05:You are no more or less likely to have the outcome as a
result of the predictor variable after controlling for *at least one* other covariate. (this would be non-
significant)
Survival Analysis: A statistical procedure that deals with investigating the time it takes for a certain event to occur
(disease, complication, death, etc.). With these analyses a research can look at simply the time it took for an event to occur
based on one IV (Kaplan Meier Analysis), or one can look at the time for an event to occur when multiple variables are
considered at once (Cox Proportional Hazard).
Commonly Associated Terms:survival, Kaplan Meier, life table, Cochran Mantel-Haenszel, Log-Rank,
Breslow, Cox Regression, Cox Proportional Hazard,survival function, rate ratio (RR), hazard ratio
(HR),odds ratio,likelihood, protective effect, risk, odds, 95% confidence interval, classification table,
dichotomous DV, backward regression, forward regression, standard/simultaneous regression,
sequential/hierarchical regression, statistical/stepwise regression.
What to interpret:OR/RR/HR (these are your measures for risk of the event occurring given the predictor
variable), p-value for OR, RR, or HR, Confidence intervals for OR/RR/HR (should not cross over 1.0, should not
be overly large e.g. 1.2 – 45.5), survival curves (if they are included).
How to interpret:
Odds Ratio/Rate Ratio/Hazard Ratio < 1: For every unit increase in the independent variable, the odds of
having the event occurring decrease by (OR) times. “Protective effect.”
Odds Ratio/Rate Ratio/Hazard Ratio > 1: For every unit increase in the independent variable, the odds of
the event occurring increase by (OR) times.
Odds Ratio/Rate Ratio/Hazard Ratio = 1 or CI crosses 1.0 or p > .05: You are no more or less likely to
have the event occurring as a result of the predictor variable (this would be non-significant).
**Sometimes covariates may be used to account for the variance in the study.
Prepared By: Tiffany Smith, Patrick Barlow, and Eric Heidel

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SENSITIVITY & SPECIFICITY
Sensitivity and Specificity are two areas of statistical analysis that are usually seen in diagnostic testing and/or the
engineering field. They provide the probability that a test will yield a correct response.
Commonly Associated Terms:
True Positive: Test classified the patient as having a disease and the patient did have the disease
True Negative: Test classified the patient as not having the disease and the patient did not have the disease.
False Positive: Test classified the patient as having a disease and the patient did not have the disease.
False Negative: Test classified the patient as not having the disease and the patient did have the disease.
Sensitivity: Proportion of patients with a disease who will test positive for it. The ability of the test to identify
positive results.
Specificity: Proportion of patients without a disease who will test negative for it. The ability of the test to identify
negative results.
Positive Predictive Value: Proportion of True Positive classifications. The ability of the test to correctly classify
patients with a disease.
Negative Predictive Value: Proportion of True Negative classifications. The Ability of the test to correctly
classify patients without a disease.
Disease State
+ -
Positive Predictive
Value
+ True Positive False Positive
Test
Results
Negative
Predictive Value
- False Negative True Negative
Sensitivity = Specificity =
Prepared By: Tiffany Smith, Patrick Barlow, and Eric Heidel

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Remember: Just because a finding is not significant does not mean that it is not meaningful. You should always consider
the effect size and context of the research when making a decision about whether or not any finding is clinically relevant.
"Absence of evidence is not evidence of absence!" -- Carl Sagan
The University of Tennessee, Knoxville Office of Medical Education, Research, and Development
Patrick Barlow, Eric Heidel, and Tiffany Smith
University of Tennessee Medical Center
Prepared By: Tiffany Smith, Patrick Barlow, and Eric Heidel