1. Recognise and identify the various scales and kinds of data (discrete and continuous, ordinal and nominal).
2. Construct, present and interpret frequency tables, and graphs (histograms, bar charts, pie charts). In addition, understand and explain the shape of various distributions (skewed and symmetric) for medical data.
3. Summarize, calculate and interpret the measures of location (mean, mode, and median) and measures of dispersion (variance, standard deviation, range). Identify extreme values and outliers and explain their significance.
4. Describe and explain the idea of: probability, experiment, event, outcome and sample space. In addition, construct the sample space given an experiment coming from the biological/medical sciences.
5. Calculate probabilities and basic relationships of probability (union of events, complement of event, intersection of events, conditional probability). Apply these in problems in biostatistics. Calculate and interpret ratios and odds.
6. Understand the concept of hypothesis testing and state the null and alternative hypothesis. Also understand the meaning of the significant level and recognise the two types or errors (type I and II).
7. Recognize the chi-square distribution and make use of its table.
8. Implement the two chi square tests, goodness of fit test and test of independence, with real data from the biological/medical sciences and interpret the results.
9. Construct the scatter diagram. Calculate and interpret the Pearson’s coefficient, and, estimate and interpret the coefficients (b0 and b1) of the simple linear regression model.

Tabular and graphical methods

Statistics in practice. Kinds of data (discrete and continuous, ordinal and nominal). Different kinds of variables. Frequency tables, cumulative distribution tables and graphs (histograms, bar charts, pie charts). Shape of various data distributions (skewed, and symmetric). Applications in Biostatistics.

Descriptive statistics: Numerical methods

Summarizing quantitative data. Measures of location (mean, mode, and median) and measures of dispersion (variance, standard deviation, range) for group data and raw data. Difference between measures of location and measures of dispersion and their significance. Extreme values, outliers and their importance in the medical sciences.

Introduction to Probability

The idea of probability. Experiments, events, outcomes and sample space. Relative frequencies. Calculation of probabilities and basic relationships of probability (union of events, complement of event, intersection of events). Ratios and odds. Conditional probability and multiplication law.

Discrete Probability Distributions

Probability distribution tables. Theory and their applications in Biostatistics.

Hypothesis testing

Hypothesis testing. Null and alternative hypothesis. Significant level. Types or errors (type I and II).

Tests of goodness of fit and independence

Chi-square distribution and its table. Chi square goodness of fit test. Contingency tables and the chi square test of association. “Statistical significance”. Applications using data from the medical sciences.

Correlation

Pearson’s coefficient.

Simple linear regression

Independent and dependent variable. Scatter diagram. Coefficients (b0 and b1) of the simple linear regression model.

• M. Pagano, K. Gauvreau, Principles of Biostatistics (Greek translation), 2002

• W.W. Daniel, Biostatistics: Basic Concepts and Methodology for the Health Sciences, Wiley, 2010

The taught part of course is delivered to the students by means of lectures, and tutorials. Lecture notes are available through the e-learning platform of the University, and the instructor’s webpage.

Students are encouraged for class work, problem solving and discussion.