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