## AP2A61-Experimentation and Data Analysis

Module Provider: School of Agriculture, Policy and Development
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Dr Lindsay Todman

Type of module:

Summary module description:

This module covers the principles of experimental design and common statistical methods for analysing datasets.

Aims:

To provide students with the ability to design experiments and to analyse and interpret the data.

Assessable learning outcomes:

Having completed this module students should be able to:

• Present data effectively in figures and tables suitable for reports and showing the variability in the data, for example using boxplots and similar, scatterplots

• Describe the concepts of: summary statistics of location and spread; sources of chance variation; frequency distribution; confidence limit; significance; analysis of variance; correlation and regression; tests and measures of association based on counts.

• Design an experiment with two factors and variation between units requiring blocking.

• Conduct analysis of variance, correlation test,  regression analysis and chi squared test with standard software and interpret the results

• Report the method and results of a statistical analysis concisely whilst providing all key information.

Critical approach to presentation and summary of data in all areas of life. Awareness of measures of uncertainty in science and their meaning. Greater numeracy. Improved confidence and selectivity with computer-based data-handling and analysis.

Outline content:

Critical understanding of experimental data is crucial to good decision-making in almost all parts of life. This module provides a working understanding of the logic of experimental design, practice in meaningful presentation of data, and the rationale of the most common statistical methods used in experiments in agricultural-related sciences.

Content includes: Data presentation methods and how they help (or otherwise) to clarify patterns and meaning; They key problem: distinguishing real effects from chance variation (i.e. signal from noise); Analysis of variance; Breaking complex questions into simpler ones; Principles of experimental design in the presence of background variation; Relationships between two (or more) measured characteristics; Critically examining patterns in count data (numbers of individuals in discrete groups).

Brief description of teaching and learning methods:

Problem/literature-driven explanations of concepts Multiple-choice tests designed to elicit and explain common misconceptions Practice in applying concepts using computer-based tools and widely used software Explanation and discussion of discipline-based published examples.

Contact hours:
 Autumn Spring Summer Lectures 10 Practicals classes and workshops 20 Guided independent study: 50 10 10 Total hours by term 80 10 10 Total hours for module 100

Summative Assessment Methods:
 Method Percentage Set exercise 50 Class test administered by School 50

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Set exercises based on practicals throughout the term

Computer-based Class test

Formative assessment methods:

In class mini-quizzes to check key concepts

Two formative set exercises based on practicals

Penalties for late submission:
The Module Convener will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

• The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall based on the criteria set out for undergraduate programmes.

Reassessment arrangements:

By coursework (computer-based test).