Course: Biometry


Upon completion of this course, the student is expected to be able to:

  • distinguish stochastic and deterministic phenomena and experiments
  • using enumeration methods and basic probability tools
  • apply simple probability calculus
  • recognize the practical value and importance of probabilities in the understanding of stochastic phenomena and experiments
  • describe and summarize data
  • translate a research question into a statistical hypothesis when given a data group and the type of experimental design or sampling procedure
  • apply estimation and testing methods  in order to make data-based decisions
  • identify the selected method’s assumptions  and keep in mind that it is required to apply checks for them
  • comprehend and interpret correctly the statistical significance
  • interpret results correctly, effectively, and in context without relying on statistical jargon
  • comprehend the notion of uncertainty which is always contained in statistical inference
  • critique data-based claims and evaluate data-based decisions
  • complete a research project that employs simple statistical inference
  • comply to ethical issues.

Course description:

1) Statistical approach: a brief overview.

2) Useful counting rules (multiplication principle, permutations, k-permutations, combinations).

3)  Practical notion of probability; basic probability tools.

4) Conditional probability (multiplication rule; law of the total probability; Bayes theorem); Independence.

5) Random variables (cumulative distribution function; discrete and continuous random variables; probability function; probability density function; mean and variance).

6) Useful discrete distributions (Bernoulli; Binomial; Poisson).

7) Useful continuous distributions

8) Central limit theorem.

9) The role of probability in statistics.

10) Descriptive statistics (frequency table; numerical descriptive measures; barchart; piechart; box plot; histograms).

11) Sampling distributions.

12) Estimation; point estimation (properties of an estimator); interval estimation (confidence intervals for a (difference of) population mean (s) or proportion (s));

13) Testing hypotheses for a (difference of) population mean (s) or proportion (s));

14) Analysis of variance (single-factor ANOVA; two-factor ANOVA).

15) Goodness-of-fit test; Chi-Square test of independence.

Teaching aids: Textbooks

Examination: Written examination