Statistics Explained An Introductory Guide for Life Scientiest

ISBN-10: 0521543169

ISBN-13: 9780521543163

Edition: 2005

Authors: Steve McKillup
List price: $45.00
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Description: The author presents an introduction to experimental design and statistics for undergraduate students in life sciences who do not have a strong mathematical background. Hypothesis testing and experimental design are discussed first, then statistical tests are explained using pictorial examples and a minimum of formulae.

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Book details

List price: $45.00
Copyright year: 2005
Publisher: Cambridge University Press
Publication date: 12/15/2005
Binding: Paperback
Pages: 280
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 0.990
Language: English

Steve McKillup is an Associate Professor of Biology in the School of Medical and Applied Sciences at Central Queensland University, Rockhampton. He has received several tertiary teaching awards, including the Vice-Chancellor's Award for Quality Teaching and an Australian Learning and Teaching Council citation 'for developing a highly successful method of teaching complex physiological and statistical concepts, and embodying that method in an innovative international textbook' (2008). He has gained a further citation for Outstanding Contributions to Student Learning, in the latest Australian Awards for University Teaching 2014. The citation has been awarded for 'developing resources that engage, empower and enable environmental science students to understand and use biostatistics', which includes his books on statistics that are being used worldwide. He is the author of Geostatistics Explained: An Introductory Guide for Earth Scientists (Cambridge, 2010).

Why do life scientists need to know about experimental design and statistics?
What is this book designed to do?
'Doing science' - hypotheses, experiments, and disproof
Basic scientific method
Making a decision about an hypothesis
Why can't an hypothesis or theory ever be proven?
'Negative' outcomes
Null and alternate hypotheses
Collecting and displaying data
Variables, experimental units, and types of data
Displaying data
Displaying ordinal or nominal scale data
Bivariate data
Multivariate data
Summary and conclusion
Introductory concepts of experimental design
Sampling - mensurative experiments
Manipulative experiments
Sometimes you can only do an unreplicated experiment
A bit of common sense
Designing a 'good' experiment
Probability helps you make a decision about your results
Statistical tests and significance levels
What has this got to do with making a decision or statistical testing?
Making the wrong decision
Other probability levels
How are probability values reported?
All statistical tests do the same basic thing
A very simple example - the chi-square test for goodness of fit
What if you get a statistic with a probability of exactly 0.05?
Statistical significance and biological significance
Summary and conclusion
Working from samples - data, populations, and statistics
Using a sample to infer the characteristics of a population
Statistical tests
The normal distribution
Samples and populations
Your sample mean may not be an accurate estimate of the population mean
What do you do when you only have data from one sample?
Why are the statistics that describe the normal distribution so important?
Distributions that are not normal
Other distributions
Other statistics that describe a distribution
Normal distributions - tests for comparing the means of one and two samples
The 95% confidence interval and 95% confidence limits
Using the Z statistic to compare a sample mean and population mean when population statistics are known
Comparing a sample mean with an expected value
Comparing the means of two related samples
Comparing the means of two independent samples
Are your data appropriate for a t test?
Distinguishing between data that should be analysed by a paired sample test or a test for two independent samples
Type 1 and Type 2 errors, power, and sample size
Type 1 error
Type 2 error
The power of a test
What sample size do you need to ensure the risk of Type 2 error is not too high?
Type 1 error, Type 2 error, and the concept of biological risk
Single factor analysis of variance
Single factor analysis of variance
An arithmetic/pictorial example
Unequal sample sizes (unbalanced designs)
An ANOVA does not tell you which particular treatments appear to be from different populations
Fixed or random effects
Multiple comparisons after ANOVA
Multiple comparison tests after a Model I ANOVA
An a-posteriori Tukey comparison following a significant result for a single factor Model I ANOVA
Other a-posteriori multiple comparison tests
Planned comparisons
Two factor analysis of variance
What does a two factor ANOVA do?
How does a two factor ANOVA analyse these data?
How does a two factor ANOVA separate out the effects of each factor and interaction?
An example of a two factor analysis of variance
Some essential cautions and important complications
Unbalanced designs
More complex designs
Important assumptions of analysis of variance: transformations and a test for equality of variances
Homogeneity of variances
Normally distributed data
Are transformations legitimate?
Tests for heteroscedasticity
Two factor analysis of variance without replication, and nested analysis of variance
Two factor ANOVA without replication
A-posteriori comparison of means after a two factor ANOVA without replication
Randomised blocks
Nested ANOVA as a special case of a one factor ANOVA
A pictorial explanation of a nested ANOVA
A final comment on ANOVA - this book is only an introduction
Relationships between variables: linear correlation and linear regression
Correlation contrasted with regression
Linear correlation
Calculation of the Pearson r statistic
Is the value of r statistically significant?
Assumptions of linear correlation
Summary and conclusion
Simple linear regression
Linear regression
Calculation of the slope of the regression line
Calculation of the intercept with the Y axis
Testing the significance of the slope and the intercept of the regression line
An example - mites that live in the your hair follicles
Predicting a value of Y from a value of X
Predicting a value of X from a value of Y
The danger of extrapolating beyond the range of data available
Assumptions of linear regression analysis
Further topics in regression
Non-parametric statistics
The danger of assuming normality when a population is grossly non-normal
The value of making a preliminary inspection of the data
Non-parametric tests for nominal scale data
Comparing observed and expected frequencies - the chi-square test for goodness of fit
Comparing proportions among two or more independent samples
Bias when there is one degree of freedom
Three-dimensional contingency tables
Inappropriate use of tests for goodness of fit and heterogeneity
Recommended tests for categorical data
Comparing proportions among two or more related samples of nominal scale data
Non-parametric tests for ratio, interval, or ordinal scale data
A non-parametric comparison between one sample and an expected distribution
Non-parametric comparisons between two independent samples
Non-parametric comparisons among more than two independent samples
Non-parametric comparisons of two related samples
Non-parametric comparisons among three or more related samples
Analysing ratio, interval, or ordinal data that show gross differences in variance among treatments and cannot be satisfactorily transformed
Non-parametric correlation analysis
Other non-parametric tests
Choosing a test
Doing science responsibly and ethically
Dealing fairly with other people's work
Doing the experiment
Evaluating and reporting results
Quality control in science
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