Avoid and detect statistical malpractice:
Design and analysis for biologists, with R
How to get on top of statistics, a course and reference-work
image copyright Shutterstock/ YAKOBCHUK VASYL, 18percentgrey
- Authors:
- Bob Brightwell and Bob Dransfield
- Publisher:
- InfluentialPoints (2013)
- Format:
- Online access from a secure folder.
1200 hypertext pages (= 4000 A4 pages). 6000 illustrations. 100's of worked examples. 1000's of notes & refs, + R-code. - Availability:
- How to get access.
- Description:
- This is a practical, up-to-date, interactive biostatistics course and reference work for undergraduate or postgraduate biologists.
- It shows how readily malpractice can be identified in statistical analyses, and critically evaluates a huge variety of medical, veterinary, and ecological examples.
- It evaluates the reasons for statistical malpractice, from ignorance to mistakes to fraud.
- It clarifies the conflicting terminology, explains the statistical controversies, and reviews common errors in design, analysis and interpretation.
- It describes the popular study designs, plus many advanced ones - and examines their reasoning, strengths and weaknesses.
- It explores the reasoning behind statistical analyses (including worked examples) and explains how they are affected by their assumptions, study design, and data.
- It uses simulation models to explore the logic and assumptions that underlie the standard formulae, and exposes their behaviour when those assumptions hold - or when they fail.If you want to publish worthwhile results, or defend your conclusions, or critically evaluate others' work, this is invaluable.
- 1). Check it will display
- Our hypertext pages can be displayed by most devices with a reasonably up-to-date browser.
- Out-of-date browsers display this arrow → as a rectangle: ▭, or like this: →
Our pages are not designed for tiny screens, very slow connections, or text-only displays.
- If you are unsure try a peep inside.
- 2). Decide if you need R
- R is not essential for reading this book, only for doing the excercises (they include an intro for those new to R).
R is a free software environment for statistical computing and graphics. For more information see the introduction to R, or the Wikipedia page.
- R requires Windows, Mac, or Linux.
- You can download the latest 'binary file for a base distribution' free.
- Base-R for Windows and base-R for MAC include a graphical user interface.
- R for Linux does not, but you can run Windows R on a virtual Windows system using VirtualBox (which is also free). - 3). Decide to buy it
- A single-user, open-ended, 100%-online, multi-machine access costs £ 25.
You can pay in most currencies, and by card, via PayPal, safely and reliably.
All orders are completed by hand, usually within 24 hrs.
We regret this publication is no longer available to new users.
How to obtain online access
 InfluentialPoints.comBiology, images, analysis, design... |
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"It has long been an axiom of mine that the little things are infinitely the most important" |
Avoid and detect statistical malpractice:
Design and analysis for biologists, with R
| Peep inside
image copyright Shutterstock/ YAKOBCHUK VASYL, 18percentgrey |
Authors: | Bob Brightwell and Bob Dransfield |
|
|
| Publisher: | InfluentialPoints (2013) | |||
| Format: | Online access from a secure folder. 1200 hypertext pages (= 4000 A4 pages). 6000 illustrations. 100's of worked examples. 1000's of notes & refs, + R-code. How to get R (it is free). |
|||
| Availability: | How to get access. | |||
| Description: | This is a practical, up-to-date, interactive biostatistics course and reference work for undergraduate or postgraduate biologists.
- It shows how readily malpractice can be identified in statistical analyses, and critically evaluates a huge variety of medical, veterinary, and ecological examples. - It evaluates the reasons for statistical malpractice, from ignorance to mistakes to fraud. - It clarifies the conflicting terminology, explains the statistical controversies, and reviews common errors in design, analysis and interpretation. - It describes the popular study designs, plus many advanced ones - and examines their reasoning, strengths and weaknesses. - It explores the reasoning behind statistical analyses (including worked examples) and explains how they are affected by their assumptions, study design, and data. - It uses simulation models to explore the logic and assumptions that underlie the standard formulae, and exposes their behaviour when those assumptions hold - or when they fail. If you want to publish worthwhile results, or defend your conclusions, or critically evaluate others' work, this is invaluable. | |||
How to obtain online access
| Our hypertext pages can be displayed by most devices with a reasonably up-to-date browser.
- Out-of-date browsers display this arrow → as a rectangle: ▭, or like this: → Our pages are not designed for tiny screens, very slow connections, or text-only displays. - If you are unsure try a peep inside. |
| |||||
| R is not essential for reading this book, only for doing the excercises (they include an intro for those new to R).
R is a free software environment for statistical computing and graphics. For more information see the introduction to R, or the Wikipedia page. | 
| |||||
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