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# PHPS41150

#### Introduction to Biostatistics (PHPS41150)

Subject:
Public Health & Population Sci
College:
Health & Agricultural Sciences
School:
Public Hlth, Phys & Sports Sci
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No

Curricular information is subject to change.

A module covering essential and fundamental principles of statistics as applied in biology, medicine, and related fields.

The use of data to describe and infer properties of biological, clinical, and other human characteristics is increasing in academic research, and in many professional settings. It is important for the integrity of any conclusions drawn that such data analysis be conducted correctly, interpreted appropriately, and that transparency of methods and openness to assessment and critique be embedded in quantitative research.

This module will introduce the student to the quantification of human characteristics, and how to best describe them using numerical summaries, and visually. We will also provide an overview of the principles of statistical inference - drawing conclusions from data and understanding the uncertainty inherent in samples of human participants. The student will also learn how to choose and use simple statistical models of data collected on samples, on groups of participants, and on serial measurements over time.

The practical use of appropriate statistical software will be taught in parallel to the more theoretical aspects of the material.

###### Learning Outcomes:

Upon completion of this module, students should be able to:

Understand and choose basic statistical analyses, including descriptive statistics, chi square and t tests, ANOVA models, correlation and simple linear regression, and selected non-parametric tests.

How to report methods and findings in accordance with best practice.

How to correctly interpret and critique the results of numerical analyses of biological, medical or related data.

###### Indicative Module Content:

- Data and descriptive statistics
- The Normal and other distributions
- Comparing means and proportions between groups: t tests and chi-square tests
- Comparing many means: between-subjects ANOVA, within-subjects ANOVA, mixed ANOVA
- Correlation
- Linear regression
- Non-parametric tests
- Error, bias and reliability
- Trends in the transparent use of statistics in research

###### Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

30

Autonomous Student Learning

54

Tutorial

12

Computer Aided Lab

12

Online Learning

12

Total

120

###### Approaches to Teaching and Learning:
This module adopts a flexible, modular and problem-based approach to learning statistics.

Each week's session begins with a lecture covering the rationale, theory and mechanism for a particular statistical method or approach. Each lecture is accompanied by interactive sessions, with questions encouraged from the class, and with clear step-by-step examples where there is any mathematics involved.

Further weekly learning continues with practical computer exercises, familiarising the student with a software package, how to interpret the output, and report it correctly. Further exercise sheets for practice are provided.
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: Computer Lab assignment Week 10 n/a Standard conversion grade scale 40% No

40

No
Assignment: End of trimester assignment Coursework (End of Trimester) n/a Standard conversion grade scale 40% No

60

No

Carry forward of passed components
Yes

Resit In Terminal Exam
Spring No