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Lismore Session 2 Session 2
Online Session 2 N/A

Unit Summary

Unit type

UG Coursework Unit

12

7

Introductory

Unit aim

Introduces statistical methods required for environmental science/management; the different types of variables, exploring data with descriptive statistics, graphically presenting data, performing tests of significance (chi-square, t-test, correlation, regression and analysis of variance) and presenting the results of statistical analyses. Emphasis is placed on matching suitable data with an appropriate statistical method in an environmental management setting and developing a "tool kit" of skills and resources to help students become independent in data management and analysis.

Unit content

• Data management skills and descriptive statistics with Microsoft Excel, scientific calculators and SPSS, including; variable types, measurements and units, number skills, rounding, significant figures, preparing data for statistical analysis, graphs and data transformation
• Measures of central tendency (mean, mode and median) and dispersion (standard error, variance and standard deviation)
• Dealing with uncertainty
• Populations and samples
• Probability distributions
• The normal distribution
• Confidence intervals
• Type I and Type II error
• Hypotheses testing for differences
• Student's t test
• Analysis of variance
• Hypotheses testing for relationships/association
• Correlation
• Linear regression
• An introduction to non-parametric equivalents of common tests
• Hypothesis testing with categorical data: Chi square
• Presenting different variable types and the results of hypothesis testing in a professional manner
• Experimental and survey design

Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

1. develop scientific questions to direct design and analysis selection
• GA1:
• GA4:
• GA5:
2. show recognition of the different types of variables used in environmental science/management, calculate means, modes, medians, standard deviations, standard error, variance, confidence intervals and chi square, using a scientific calculator and Microsoft Excel
• GA4:
3. state the null hypothesis, prepare data for statistical analysis, calculate required values and interpret the results of the following statistical tests, chi square, t test, correlation, regression and analysis of variance for research questions relevant to environmental science/management
• GA1:
• GA4:
4. present the results of statistical tests of significance in a professional manner, including using Microsoft Excel or SPSS to produce high quality graphs (appropriate to the type of variables presented)
• GA1:
• GA4:
• GA5:
5. explain the consequences of Type I and Type II errors in tests of significance in context of environmental science/management and demonstrate a basic understanding of the validation of a chosen statistical method
• GA1:
• GA4:
• GA5:
6. propose a suitable research question, develop methodologies that would lead to collection of the correct type of data and choose appropriate statistical tests for an investigation relevant to environmental science/management
• GA1:
• GA4:
• GA5:
7. explain the difference between a population and a sample and demonstrate a basic understanding of key concepts, symbols and terms relevant to survey and experimental design.
• GA1:
• GA4:

Prescribed texts

• All students will need a print copy of this textbook. This textbook provides the scaffolding around which we will build the toolkit of skills you will require to complete all assessment items and for the remainder of your degree: Ennos, R & Johnson, M, 2018, Statistical and Data Handling Skills in Biology, 4th edn, Pearson United Kingdom, Harlow, England. ISBN: 9781292086033.
Prescribed texts may change in future teaching periods.