Module: Introduction to Biostatistics

Module Specifications

Academic Year 2025 - 2026

Module Title	Introduction to Biostatistics
Module Code	STA1001 (ITS: BE114)
Faculty	Science & Health	School	Biotechnology
NFQ level	8	Credit Rating	5

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.

Learning Outcomes

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2. Construct appropriate graphical summaries for a sample of data, including dot-plots, box-plots.
5. 1
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7. Calculate numerical summaries for a sample of data, including the mean, standard deviation, median and quartiles.
10. 2
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12. Use simple counting and combinatorial arguments to calculate probabilities
15. 3
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17. Calculate probabilities for combinations of events, including unions, intersections and complements, using the laws of probability
20. 4
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22. Calculate means, variances and probability distribution of random variables
25. 5
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27. Calculate marginal and conditional distributions, calculate the correlation, assess independence
30. 6
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32. Calculate probabilities from standard distributions (Binomial, Poisson, Normal)
35. 7
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37. Use R to explore data both numerically and graphically
40. 8
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42. Formulate a null and alternative hypothesis and calculate significance using an appropriate test for both normal and non parametric data.
45. 9
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47. Calculate the sensitivity, specificity and power of a test.
50. 10

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	No Description
Tutorial	12	No Description
Directed learning	37.5	No Description
Independent Study	51.5	No Description
Total Workload: 125

Section Breakdown
CRN	10131	Part of Term	Semester 1
Coursework	30%	Examination Weight	70%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Daniel Murphy	Module Teacher	Denis Collins, Emma Finlay, Finbarr O'Sullivan, Gaetan Thilliez

Type	Description	% of total	Assessment Date
Assessment Breakdown
Loop Quiz	Weekly tutorials	10%	n/a
Assignment	R assignment	15%	n/a
Assignment	R assignment	15%	n/a
Written Exam	End of term exam	60%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

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

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None