OVERVIEW CONCEPT LIST STATISTICS:
Methodology & Descriptive Statistics
Population & sample
Parameters and statistics
Random sample and sampling variability
Measurement scales (nominal, ordinal, interval and ratio)
Descriptive statistics (selecting appropriate ones)
Mean? Median? Mode? SD? IQR?
Frequency distributions
Univariate and Bivariate Graphical displays (selecting the proper ones, a fundamental part of the
Exploratory Data Analysis)
Research Designs (experimental and observational)
Units of observation
Threats to Internal and External Validity of research studies (the infamous limitations)
The z-score - The Normal distribution
Pearson’s correlation Coefficient
Relative Risk (RR), Odds Ratio (OR)
Measuring Population Health (Life Expectancy, Mortality, DALYs QALYs etc)
Inferential Statistics (It is important to know when to select which test and their assumptions)
Null Hypothesis testing/ Confidence Intervals/p-values
The t-tests (independent and paired)
ANOVA
The Chi-Square test
The simple and multiple linear regression
The simple and multiple logistic regression
Important: you need to know how to interpret the model's parameters (in logistic and linear
context)
Dummy Variables
Confounding & Interaction
Multicollinearity
Variables Selection (backward, forward, top-down)
Factor Analysis
Relaibility and Validity of a questionnaire
Questionnaire Construction
How to interpret SPSS output of all techniques
How to learn: a good start is the experience you went through when writing the reports and
doing the critical appraisal (revise them, check the corrective feedback given, especially the
frequent statistical mistakes). GO as well through the lecture sheets and try to run some
analyses with SPSS of the additional exercises that you did not have time to do during the
training (check SPSS manuals).
Do not forget to read the papers uploaded in ELeum (articles for the MC questions). Make
sure you understand tables, statistics, graphs etc…
, Test questions examples (multiple choice questions statistics)
Question 1: What do the authors mean with ‘adjustment for age, physical activity, smoking and
percent of body fat’ via the multiple regression models?
a. Age, physical activity smoking and percent of body fat are significant predictors of body fat
but not potential confounders
b. They want to test for possible interactions between alcohol consumption and the variables
age, physical activity, smoking and percent of body fat
c. They want an independent alcohol effect on body fat, disentangled from the effect of
variables age, physical activity, smoking and percent of body fat
d. ‘Adjustment’ means that the variables age, physical activity, smoking and percent of body fat
were not included in the model
Methodology & Descriptive Statistics
Population & sample
Parameters and statistics
Random sample and sampling variability
Measurement scales (nominal, ordinal, interval and ratio)
Descriptive statistics (selecting appropriate ones)
Mean? Median? Mode? SD? IQR?
Frequency distributions
Univariate and Bivariate Graphical displays (selecting the proper ones, a fundamental part of the
Exploratory Data Analysis)
Research Designs (experimental and observational)
Units of observation
Threats to Internal and External Validity of research studies (the infamous limitations)
The z-score - The Normal distribution
Pearson’s correlation Coefficient
Relative Risk (RR), Odds Ratio (OR)
Measuring Population Health (Life Expectancy, Mortality, DALYs QALYs etc)
Inferential Statistics (It is important to know when to select which test and their assumptions)
Null Hypothesis testing/ Confidence Intervals/p-values
The t-tests (independent and paired)
ANOVA
The Chi-Square test
The simple and multiple linear regression
The simple and multiple logistic regression
Important: you need to know how to interpret the model's parameters (in logistic and linear
context)
Dummy Variables
Confounding & Interaction
Multicollinearity
Variables Selection (backward, forward, top-down)
Factor Analysis
Relaibility and Validity of a questionnaire
Questionnaire Construction
How to interpret SPSS output of all techniques
How to learn: a good start is the experience you went through when writing the reports and
doing the critical appraisal (revise them, check the corrective feedback given, especially the
frequent statistical mistakes). GO as well through the lecture sheets and try to run some
analyses with SPSS of the additional exercises that you did not have time to do during the
training (check SPSS manuals).
Do not forget to read the papers uploaded in ELeum (articles for the MC questions). Make
sure you understand tables, statistics, graphs etc…
, Test questions examples (multiple choice questions statistics)
Question 1: What do the authors mean with ‘adjustment for age, physical activity, smoking and
percent of body fat’ via the multiple regression models?
a. Age, physical activity smoking and percent of body fat are significant predictors of body fat
but not potential confounders
b. They want to test for possible interactions between alcohol consumption and the variables
age, physical activity, smoking and percent of body fat
c. They want an independent alcohol effect on body fat, disentangled from the effect of
variables age, physical activity, smoking and percent of body fat
d. ‘Adjustment’ means that the variables age, physical activity, smoking and percent of body fat
were not included in the model
