This module will provide you with a solid foundation in practical data analysis and interpretation. It will 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. You will be able link the theory presented with the practical sessions and data collection components and will collect and analyse your own data. By the end of the module, you 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 you with the statistical knowledge to conduct in-depth data analysis for their final year research project.
Lecture 9, Workshop (PC Sessions) 18, Fieldwork (data collection) 5
1,000 words Report. Assessment Details: Statistics worksheet worth 40%.
2,000 words Report. Assessment Details: Statistics write-up and paper worth 60%.
Reassessment Method: Like-for-like
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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