Mohammed Meah - Essential Statistics for Bioscientists

1 Cover

2 Title page

3 Copyright

4 Table of Contents

5 ACKNOWLEDGEMENTS

6 LIST OF WORKED EXAMPLES OF STATISTICAL TESTS

7 Begin Reading

8 Appendix 1 Historical Landmarks in Statistics

9 Appendix 2 Common Statistical Terms

10 Appendix 3 Common Symbols Used in Statistics

11 Appendix 4 Standard Formulas

12 Appendix 5 How to Calculate Sample Size

13 Appendix 6 Familiarisation with GraphPad Prism

14 Appendix 7 Answers to Sample Problems

15 Appendix 8 Standard Critical Tables

16 REFERENCES

17 INDEX

18 End User License Agreement

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I would like to thank my daughters (Maryam and Zaynah) for their constant encouragement and support during the writing of this book. I would like to thank the many project students over the years whose research project ideas have challenged me to delve into statistics. I have to say a special thank you to my mother (BR Chowdhury) and father (MM Chowdhury) for always being positive and supportive. Many thanks to my colleagues (Rane and Elizabeth) for their insightful comments and suggestions.

Lastly, I would like to thank Wiley Publishers for their encouragement and above all patience and understanding in the completion of this book.

“If it’s green or wriggles, it’s biology. If it stinks, it’s chemistry. If it doesn’t work, it’s physics or engineering. If it’s green and wiggles and stinks and still doesn’t work, it’s psychology. If it’s incomprehensible, it’s mathematics. If it puts you to sleep, it’s statistics.”

— Anonymous

“All life is an experiment. The more experiments you make, the better.”

Ralph Waldo Emerson (1803–1882) - American lecturer, philosopher and poet

The word statistics is derived from the Latin word ‘status’ – meaning political state or a government. Statistics deals with collection, organization, presentation, analysis and interpretation of data to obtain meaningful and useful information. Statistics can be split into two major areas, namely, descriptive and inferential. Descriptive statistics involves collecting, summarizing, and presenting data. Inferential statistics involves analysing sample data to draw conclusions about a population.

Statistics is an area which is often much reduced in the curriculum of undergraduate bioscience degree courses. Statistics tends to be linked to research modules. Lecturers often assume that students have a strong grasp of mathematical and statistical concepts including data analysis. However, the reality is that most students are ‘rusty’ in these areas, particularly in statistics. The most urgent need for statistics is usually for the research project which is typically in the final year of the undergraduate degree (level 6). It is unclear, during undergraduate studies, how much and when statistics should be taught. In addition, there are a variety of software packages which can be used to perform statistical analysis, and display data, not all of which can be accessed or used competently by the students. Indeed, it would be fair to say that existing software can produce extensive statistical analysis, but choosing an appropriate test and interpreting the data analysis can be challenging. It is rare to have the luxury to be able to consult a resident statistician in the Bioscience Department.

There are a variety of statistical software packages, which vary in the difficulty of use, and in what tests they can perform. An additional bonus is the ability to plot graphically, mean and individual data. The most popular software packages used currently to perform statistics and present data in graphical form are Excel (Microsoft), Prism (GraphPad) and SPSS (IBM). Microsoft Excel is a popular spreadsheet software package which is easily available, easy to use for data analysis (although types of analysis are limited), and useful to plot data graphically (limited in detail of graph). Prism is good for statistical analysis but excellent for plotting data (graphs produced are of professional standard). SPSS is the most complex, but most comprehensive statistical package. It allows a very detailed analysis of data using a wide range of tests. However, it is weak in interpreting the statistical analysis and the level of detail in plotting graphs.

A core module that most students would do is a research project. This requires them to put forward a research proposal, in which they design experiments and formulate hypotheses, collect data, analyse data, and then write a research report. From my many years of supervising undergraduates and postgraduate projects, I have observed that firstly, narrowing a project down to a specific aim and secondly, applying statistical analysis to the data obtained causes the most anxiety in students.