Students often approach statistics with great apprehension. For many, it is a required course to be taken only under the most favorable circumstances, such as during a quarter or semester when carrying a light course load; for others, it is as distasteful as a visit to a credit counselor—to be postponed as long as possible, with the vague hope that mounting debts might miraculously disappear. Much of this apprehension doubtless rests on the widespread fear of mathematics and mathematically related areas.
This book is written to help you overcome any fear about statistics. Unnecessary quantitative considerations have been eliminated. When not obscured by mathematical treatments better reserved for more advanced books, some of the beauty of statistics, as well as its everyday usefulness, becomes more apparent.
You could go through life quite successfully without ever learning statistics. Having learned some statistics, however, you will be less likely to flinch and change the topic when numbers enter a discussion; you will be more skeptical of conclusions based on loose or erroneous interpretations of sets of numbers; you might even be more inclined to initiate a statistical analysis of some problem within your special area of interest.
To The Instructor
Largely because they panic at the prospect of any math beyond long division, many students view the introductory statistics class as cruel and unjust punishment. A halfdozen years of experimentation, first with assorted handouts and then with an extensive set of lecture notes distributed as a second text, convinced us that a book could be written for these students. Representing the culmination of this effort, the present book provides a simple overview of descriptive and inferential statistics for mathematically unsophisticated students in the behavioral sciences, social sciences, health sciences, and education.
- Basic concepts and procedures are explained in plain English, and a special effort has been made to clarify such perennially mystifying topics as the standard deviation, normal curve applications, hypothesis tests, degrees of freedom, and analysis of variance. For example, the standard deviation is more than a formula; it roughly reflects the average amount by which individual observations deviate from their mean.
- Unnecessary math, computational busy work, and subtle technical distinctions are avoided without sacrificing either accuracy or realism. Small batches of data define most computational tasks. Single examples permeate entire chapters or even several related chapters, serving as handy frames of reference for new concepts and procedures.
- Each chapter begins with a preview and ends with a summary, lists of important terms and key equations, and review questions.
- Key statements appear in bold type, and step-by-step summaries of important procedures, such as solving normal curve problems, appear in boxes.
- Important definitions and reminders about key points appear in page margins.
- Scattered throughout the book are examples of computer outputs for three of the most prevalent programs: Minitab, SPSS, and SAS. These outputs can be either ignored or expanded without disrupting the continuity of the text.
- Questions are introduced within chapters, often section by section, as Progress Checks. They are designed to minimize the cumulative confusion reported by many students for some chapters and by some students for most chapters. Each chapter ends with Review Questions.
- Questions have been selected to appeal to student interests: for example, probability calculations, based on design flaws, that re-create the chillingly high likelihood of the Challenger shuttle catastrophe (8.18, page 165); a t test analysis of global temperatures to evaluate a possible greenhouse effect (13.7, page 244); and a chi-square test of the survival rates of cabin and steerage passengers aboard the Titanic (19.14, page 384).
- Appendix B supplies answers to questions marked with asterisks. Other appendices provide a practical math review complete with self-diagnostic tests, a glossary of important terms, and tables for important statistical distributions.
Part 1. Descriptive statistics: organizing and summarizing data
2. Describing data with tables and graphs 22 tables (frequency distributions)
3. Describing data with averages
4. Describing variability
5. Normal distributions and standard (z) scores
6. Describing relationships: correlation
Part 2. Inferential statistics: generalizing beyond data
8. Populations, samples, and probability
9. Sampling distribution of the mean
10. Introduction to hypothesis testing: the z test
11. More about hypothesis testing
12. Estimation (confidence intervals)
13. t Test for one sample
14. t Test for two independent samples
15. t Test for two related samples (repeated measures)
16. Analysis of variance (one factor)
17. Analysis of variance (repeated measures)
18. Analysis of variance (two factors)
19. Chi-square ( X 2) test for qualitative (nominal) data
20. Tests for ranked (ordinal) data
21. Postscript: which test?
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