Essential Statistics for the Pharmaceutical Sciences

ISBN-10: 0470034688

ISBN-13: 9780470034682

Edition: 2007

Authors: Philip Rowe
List price: $84.00 Buy it from $17.13
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Description: "Essential Statistics for the Pharmaceutical Sciences" is a clear, accessible introduction to the key statistical techniques employed for the analysis of data within this subject area. Written in a concise and logical manner, the book explains why statistics are necessary and discusses the issues that experimentalists need to consider. The reader is carefully taken through the whole process, from planning an experiment to interpreting the results, avoiding unnecessary calculation methodology. The most commonly used statistical methods are described in terms of their purpose, when they should be used and what they mean once they have been performed. Numerous examples are provided throughout the text, all within a pharmaceutical context, with key points highlighted in summary boxes to aid student understanding. "Essential Statistics for the Pharmaceutical Sciences" takes a new and innovative approach to statistics with an informal style that will appeal to the reader who finds statistics a challenge! This book is an invaluable introduction to statistics for any science student. It is an essential text for students taking biomedical or pharmaceutical-based science degrees and also a useful guide for researchers.

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Book details

List price: $84.00
Copyright year: 2007
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/16/2007
Binding: Paperback
Pages: 308
Size: 7.00" wide x 10.00" long x 1.00" tall
Weight: 1.298
Language: English

Preface
Statistical packages
Data Types
Data types
Does it really matter?
Interval scale data
Ordinal scale data
Nominal scale data
Structure of this book
Chapter summary
Interval-Scale Data
Descriptive statistics
Summarizing data sets
Indicators of central tendency. mean, median and mode
Describing variability. standard deviation and coefficient of variation
Quartiles. another way to describe data
Using computer packages to generate descriptive statistics
Chapter summary
The normal distribution
What is a normal distribution?
Identifying data that are not normally distributed
Proportions of individuals within one or two standard deviations of the mean
Chapter summary
Sampling from populations. the SEM
Samples and populations
From sample to population
Types of sampling error
What factors control the extent of random sampling error?
Estimating likely sampling error. The SEM
Offsetting sample size against standard deviation
Chapter summary
Ninety-five per cent confidence interval for the mean
What is a confidence interval?
How wide should the interval be?
What do we mean by '95 per cent' confidence?
Calculating the interval width
A long series of samples and 95 per cent confidence intervals
How sensitive is the width of the confidence interval to changes in the SD, the sample size or the required level of confidence?
Two statements
One-sided 95 per cent confidence intervals
The 95 per cent confidence interval for the difference between two treatments
The need for data to follow a normal distribution and data transformation
Chapter summary
The two-sample t-test(1).Introducing hypothesis tests
The two-sample t-test. an example of a hypothesis test
'Significance'
The risk of a false positive finding
What factors will influence whether or not we obtain a significant outcome?
Requirements for applying a two-sample t-test
Chapter summary
The two-sample t-test(2).The dreaded P value
Measuring how significant a result is
P values
Two ways to define significance?
Obtaining the P value
P values or 95 per cent confidence intervals?
Chapter summary
The two-sample t-test(3).False negatives, power and necessary sample sizes
What else could possibly go wrong?
Power
Calculating necessary sample size
Chapter summary
The two-sample t-test(4).Statistical significance, practical significance and equivalence
Practical significance. is the difference big enough to matter?
Equivalence testing
Non-inferiority testing
P values are less informative and can be positively misleading
Setting equivalence limits prior to experimentation
Chapter summary
The two-sample t-test(5).One-sided testing
Looking for a change in a specified direction
Protection against false positives
Temptation!
Using a computer package to carry out a one-sided test
Should one-sided tests be used more commonly?
Chapter summary
What does a statistically significant result really tell us?
Interpreting statistical significance
Starting from extreme scepticism
Chapter summary
The paired t-test. comparing two related s
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