Latest Module Specifications
Current Academic Year 2025 - 2026
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Description This course discusses the nature of biological data and the use of probability to describe biological phenomena. Topics covered include: biological data sources, data presentation, numerical and graphical summaries, basic ideas of probability, conditional probability and indpendence, random variables, standard discrete distributions, mean and variance, joint distributions, an introduction to the normal distribution for biologists and the concepts of hypothesis testing, correction for multiple testing and power and nonparametric hypothesis testing. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Construct appropriate graphical summaries for a sample of data, including dot-plots, box-plots. 2. Calculate numerical summaries for a sample of data, including the mean, standard deviation, median and quartiles. 3. Use simple counting and combinatorial arguments to calculate probabilities 4. Calculate probabilities for combinations of events, including unions, intersections and complements, using the laws of probability 5. Calculate means, variances and probability distribution of random variables 6. Calculate marginal and conditional distributions, calculate the correlation, assess independence 7. Calculate probabilities from standard distributions (Binomial, Poisson, Normal) 8. Use R to explore data both numerically and graphically 9. Formulate a null and alternative hypothesis and calculate significance using an appropriate test for both normal and non parametric data. 10. Calculate the sensitivity, specificity and power of a test. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and Learning Activities
This module provides a basic introduction to the ideas of probability . The topics are: 1. Sources of data, sampling, experiments, random variation 2. Exploring data - graphical and numerical summaries 3. Basic notions of probability - sample spaces, events, combination of events, counting 4. Conditional probability and independence, Bayes' Theorem 5. Random variables and probability distributions 6. Binomial and related probability distributions 7. Poisson distribution for counts, events over time 8. Expectation - mean and variance 9. Bivariate distributions - marginal and conditional probabilities, correlation and independence 10. Normal distribution - properties, use of tables, central limit theorem and approximations 11. Use of R for data exploration and probability calculations | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books: None Articles: None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||