This module is designed to introduce and re-affirm statistical concepts, and their correct use. This module is delivered through combined lecture/practicals using computer software. Introductory topics will include types of data, descriptive statistics such as measures of central tendency, frequency distributions, the normal distribution, variance (standard error, standard deviation), and how sample parameters and null hypotheses apply in real data. Inferential statistics include analysis of differences between two groups (e.g. t-tests and non-parametric equivalents), differences between multiple groups (ANOVA and non-parametric equivalents), variable relationships (correlation and regression), and variable associations (e.g. chi-squared test). The role of probability in data analysis will also be considered, as will its application to scientific questions. Throughout, emphasis will be placed on practical application of statistics, and when and how they are applied. Students will be able link the theory presented with the practical sessions and data collection components. As such, students will collect and analyse their own data. By the end of the module, students will have a knowledge of the underlying principles of statistics, be able to conduct statistical tests in statistical software, critically evaluate the results, and have a sound appreciation of the benefits and limitations of different statistical techniques. This module provides students with the statistical knowledge to conduct in-depth data analysis for their final year research project.
Private Study: 126
Contact Hours: 24
• BSc Wildlife Conservation
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Cannot be taken with WCON5380 and ANTB5990
Not available as an elective module
Method of assessment
Main assessment methods
Statistics worksheet (40%)
Full statistics write-up and paper (60%)
Reassessment Instrument: Like for Like
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
On successfully completing the module students will be able to:
1 Understand the theoretical normal distribution, null hypotheses, type I and II errors and their application to data analysis
2 Demonstrate an in-depth understanding of statistics and data handling, including use of appropriate computer software
3 Use parametric and non-parametric tests, including t-tests, Mann-Whitney, Chi-Square, Analysis of Variance (ANOVA), Kruskal-Wallis, regressions and correlations
4 Understand scientific methods including hypothesis building, data collection, data analysis, and critical interpretation.
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Credit level 5. Intermediate level module usually taken in Stage 2 of an undergraduate degree.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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