| Location of Tables for Tests of Significance | |
| Preface | |
| Biostatistics and Clinical Practice | p. 1 |
| The Changing Medical Environment | p. 1 |
| What Do Statistical Procedures Tell You? | p. 4 |
| Why Not Depend on the Journals? | p. 6 |
| Why Has the Problem Persisted? | p. 9 |
| How to Summarize Data | p. 10 |
| The Mean | p. 12 |
| Measures of Variability | p. 13 |
| The Normal Distribution | p. 14 |
| Percentiles | p. 15 |
| How to Make Estimates from a Limited Sample | p. 20 |
| How Good Are These Estimates? | p. 21 |
| How to Test for Difference between Groups | p. 31 |
| The General Approach | p. 31 |
| Two Different Estimates of the Population Variance | p. 36 |
| What Is a "Big" F? | p. 38 |
| Three Examples | p. 46 |
| The Special Case of Two Groups: The t Test | p. 65 |
| The General Approach | p. 67 |
| The Standard Deviation of a Difference or a Sum | p. 69 |
| Use of t to Test Hypotheses about Two Groups | p. 72 |
| What If the Two Samples Are Not the Same Size? | p. 79 |
| The Examples Revisited | p. 80 |
| The t Test Is an Analysis of Variance | p. 84 |
| Common Errors in the Use of the t Test and How to Compensate for Them | p. 86 |
| How to Use t Tests to Isolate Differences between Groups in Analysis of Variance | p. 89 |
| Other Approaches to Multiple Comparison Testing: The Student-Newman-Keuls Test | p. 95 |
| Which Multiple Comparison Procedure Should You Use? | p. 101 |
| Multiple Comparisons against a Single Control | p. 101 |
| The Meaning of P | p. 107 |
| How to Analyze Rates and Proportions | p. 113 |
| Back to Mars | p. 114 |
| Estimating Proportions from Samples | p. 119 |
| Hypothesis Tests for Proportions | p. 123 |
| Another Approach to Testing Nominal Data: Analysis of Contingency Tables | p. 132 |
| Chi-Square Applications to Experiments with More Than Two Treatments or Outcomes | p. 139 |
| The Fisher Exact Test | p. 144 |
| Measures of Association Between Two Nominal Variables | p. 149 |
| What Does "Not Significant" Really Mean? | p. 164 |
| An Effective Diuretic | p. 165 |
| Two Types of Errors | p. 169 |
| What Determines a Test's Power? | p. 171 |
| Power and Sample Size for Analysis of Variance | p. 184 |
| Power and Sample Size for Comparing Two Proportions | p. 188 |
| Power and Sample Size for Relative Risk and Odds Ratio | p. 192 |
| Power and Sample Size for Contingency Tables | p. 193 |
| Practical Problems in Using Power | p. 195 |
| What Difference Does It Make? | p. 195 |
| Confidence Intervals | p. 199 |
| The Size of the Treatment Effect Measured as the Difference of Two Means | p. 200 |
| The Effective Diuretic | p. 203 |
| What Does "Confidence" Mean? | p. 207 |
| Confidence Intervals Can Be Used to Test Hypotheses | p. 209 |
| Confidence Interval for the Population Mean | p. 211 |
| The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportions | p. 212 |
| Confidence Interval for Rates and Proportions | p. 217 |
| Confidence Intervals for Relative Risk and Odds Ratio | p. 222 |
| Confidence Interval for the Entire Population | p. 224 |
| How to Test for Trends | p. 230 |
| More about the Martians | p. 231 |
| How to Estimate the Trend from a Sample | p. 238 |
| How to Compare Two Regression Lines | p. 254 |
| Correlation and Correlation Coefficients | p. 262 |
| The Spearman Rank Correlation Coefficient | p. 273 |
| Power and Sample Size in Regression and Correlation | p. 280 |
| Comparing Two Different Measurements of the Same Thing: The Bland-Altman Method | p. 282 |
| Experiments When Each Subject Receives More than One Treatment | p. 298 |
| Experiments When Subjects Are Observed before and after a Single Treatment: The Paired t Test | p. 299 |
| Another Approach to Analysis of Variance | p. 307 |
| Experiments When Subjects Are Observed after Many Treatments: Repeated-Measures Analysis of Variance | p. 318 |
| Experiments When Outcomes Are Measured on a Nominal Scale: McNemar's Test | p. 330 |
| Alternatives to Analysis of Variance and the t Test Based on Ranks | p. 339 |
| How to Choose between Parametric and Nonparametric Methods | p. 340 |
| Two Different Samples: The Mann-Whitney Rank-Sum Test | p. 343 |
| Each Subject Observed before and after One Treatment: The Wilcoxon Signed-Rank Test | p. 354 |
| Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis Statistic | p. 362 |
| Experiments in Which Each Subject Receives More than One Treatment: The Friedman Test | p. 370 |
| How to Analyze Survival Data | p. 387 |
| Censoring on Pluto | p. 388 |
| Estimating the Survival Curve | p. 391 |
| Comparing Two Survival Curves | p. 400 |
| Gehan's Test | p. 409 |
| Power and Sample Size | p. 411 |
| What Do the Data Really Show? | p. 416 |
| When to Use Which Test | p. 417 |
| Randomize and Control | p. 419 |
| Does Randomization Ensure Correct Conclusions? | p. 427 |
| Problems with the Population | p. 432 |
| How You Can Improve Things | p. 434 |
| App. A: Computational Forms | p. 438 |
| App. B: Power Charts | p. 444 |
| App. C: Answers to Exercises | p. 453 |
| Index | p. 469 |
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