book

Statistics For Dummies®, 2nd Edition

by Deborah J. Rumsey

May 2011

Beginner

384 pages

10h 48m

English

For Dummies

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About This BookConventions Used in This BookWhat You're Not to ReadFoolish AssumptionsHow This Book Is OrganizedIcons Used in This BookWhere to Go from Here

Thriving in a Statistical WorldDesigning Appropriate StudiesCollecting Quality DataCreating Effective SummariesDetermining DistributionsPerforming Proper AnalysesDrawing Credible ConclusionsBecoming a Sleuth, Not a Skeptic
Statistics and the Media: More Questions than Answers?Using Statistics at Work
Detecting Errors, Exaggerations, and Just Plain LiesFeeling the Impact of Misleading Statistics
Statistics: More than Just NumbersGrabbing Some Basic Statistical Jargon
Summing Up Data with Descriptive StatisticsCrunching Categorical Data: Tables and PercentsMeasuring the Center with Mean and MedianAccounting for VariationExamining the Empirical Rule (68-95-99.7)Measuring Relative Standing with Percentiles
Take Another Little Piece of My Pie ChartRaising the Bar on Bar Graphs
Handling HistogramsExamining BoxplotsTackling Time Charts
Defining a Random VariableIdentifying a BinomialFinding Binomial Probabilities Using a FormulaFinding Probabilities Using the Binomial TableChecking Out the Mean and Standard Deviation of the Binomial
Exploring the Basics of the Normal DistributionMeeting the Standard Normal (Z-) DistributionFinding Probabilities for a Normal DistributionFinding X When You Know the PercentNormal Approximation to the Binomial
Basics of the t-DistributionUsing the t-TableStudying Behavior Using the t-Table
Defining a Sampling DistributionThe Mean of a Sampling DistributionMeasuring Standard ErrorLooking at the Shape of a Sampling DistributionFinding Probabilities for the Sample MeanThe Sampling Distribution of the Sample ProportionFinding Probabilities for the Sample Proportion
Seeing the Importance of That Plus or MinusFinding the Margin of Error: A General FormulaCalculating margin of error for a sample meanDetermining the Impact of Sample Size
Not All Estimates Are Created EqualLinking a Statistic to a ParameterGetting with the JargonInterpreting Results with ConfidenceZooming In on WidthChoosing a Confidence LevelFactoring In the Sample SizeCounting On Population VariabilityCalculating a Confidence Interval for a Population MeanFiguring Out What Sample Size You NeedDetermining the Confidence Interval for One Population ProportionCreating a Confidence Interval for the Difference of Two MeansEstimating the Difference of Two ProportionsSpotting Misleading Confidence Intervals
Setting Up the HypothesesGathering Good Evidence (Data)Compiling the Evidence: The Test StatisticWeighing the Evidence and Making Decisions: p-ValuesMaking ConclusionsAssessing the Chance of a Wrong Decision
Testing One Population MeanHandling Small Samples and Unknown Standard Deviations: The t-TestTesting One Population ProportionComparing Two (Independent) Population AveragesTesting for an Average Difference (The Paired t-Test)Comparing Two Population Proportions
Recognizing the Impact of PollsBehind the Scenes: The Ins and Outs of Surveys
Boiling Down the Basics of StudiesDesigning a Good ExperimentMaking Informed Decisions
Picturing a Relationship with a ScatterplotQuantifying Linear Relationships Using the CorrelationWorking with Linear RegressionMaking Proper PredictionsExplaining the Relationship: Correlation versus Cause and Effect
Organizing a two-say TableInterpreting two-say TablesChecking Independence and Describing DependenceCautiously Interpreting Results
Pinpoint Misleading GraphsUncover Biased DataSearch for a Margin of ErrorIdentify Non-Random SamplesSniff Out Missing Sample SizesDetect Misinterpreted CorrelationsReveal Confounding VariablesInspect the NumbersReport Selective ReportingExpose the Anecdote
Know What You Don't Know, and then Do Something about ItAvoid “Yeah-Yeah” TrapsMake Friends with FormulasMake an “If-Then-How” ChartFigure Out What the Question Is AskingLabel What You're GivenDraw a PictureMake the Connection and Solve the ProblemDo the Math — TwiceAnalyze Your Answers
The Z-TableThe t-TableThe Binomial Table

Content preview from Statistics For Dummies®, 2nd Edition

Chapter 17 Experiments: Medical Breakthroughs or Misleading Results?

In This Chapter

Distinguishing experiments from observational studies
Dissecting the criteria for a good experiment
Watching for misleading results

Medical breakthroughs seem to come and go quickly. One day you hear about a promising new treatment for a disease, only to find out later that the drug didn't live up to expectations in the last stage of testing. Pharmaceutical companies bombard TV viewers with commercials for pills, sending millions of people to their doctors clamoring for the latest and greatest cures for their ills, sometimes without even knowing what the drugs are for. Anyone can search the Internet for details about any type of ailment, disease, or symptom and come up with tons of information and advice. But how much can you really believe? And how do you decide which options are best for you if you get sick, need surgery, or have an emergency?

In this chapter, you go behind the scenes of experiments, the driving force of medical studies and other investigations in which comparisons are made — comparisons that test, for example, which building materials are best, which soft drink teens prefer, and so on. You find out the difference between experiments and observational studies and discover what experiments can do for you, how they're supposed to be done, how they can go wrong, and how you can spot misleading results. With so many headlines, sound bites, and pieces of “expert advice” coming ...