Studies Show: A Popular Guide to Understanding Scientific Studies
If you're not sure what to make of all the claims and counterclaims, this new book will help cut through the conflicting reports and contradictory findings.
We are bombarded daily with media reports of startling new findings from "just released" studies often in major, authoritative publications on consumer products, medications, foods, alcohol, safety devices, social behavior, public policy, and much more. The decisions of millions of consumers, professionals, and government agencies can be influenced by just one study.
Light, humorous, and entertaining, Studies Show reviews sample studies to expose their traps and pitfalls. In plain English, statistics analyst John H. Fennick discusses the methods of good and bad studies to explain how scientific results can differ sometimes radically. Fennick shows that when armed with common sense and critical intelligence, we can understand almost any study.
56 pages matching analysis in this book
Results 1-3 of 56
What people are saying - Write a review
We haven't found any reviews in the usual places.
actually analysis appear average calculated cancer censored chance cholesterol coefficient coefficient of determination column confidence interval consider control group correlation course curve dataset death rate distribution drinking effect error estimate estrogen example fact figure Framingham happen Hawthorne Effect hazard model heart disease Heart Transplant ignore included increased kind kyphosis lesson linear linear regression logistic model look mathematical mean measure never nontransplanted normal normal distribution null null hypothesis number of deaths Number of Subjects obvious odds ratio P-values patients percent picture predict probably problem published question random rats reason regression relative risk researchers risk factor S-curve sample size sample sizes scatter plot sigma significance level simply smoking speed squares statistical studies statisticians straight line survival survivors syrup things tion total number transplant variables variance zero