1 / 26

Effective Experiment Design Techniques for Reliable Results

Understand good and poor ways to experiment, key elements, control groups, randomization, blinding, and various study designs for valid outcomes. Real-world study examples included.

millerpeter
Download Presentation

Effective Experiment Design Techniques for Reliable Results

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Section 4.3 What Are Good Ways and Poor Ways to Experiment?

  2. An Experiment • Assign each subject (called an experimental unit ) to an experimental condition, called a treatment • Observe the outcome on the response variable • Investigate the association – how the treatment affects the response

  3. Elements of a Good Experiment • Primary treatment of interest • Secondary treatment for comparison • Comparing the primary treatment results to the secondary treatment results help to analyze the effectiveness of the primary treatment

  4. Control Group • Subjects assigned to the secondary treatment are called the control group • The secondary treatment could be a placebo or it could be an actual treatment

  5. Randomization in an Experiment • It is important to randomly assign subjects to the primary treatment and to the secondary (control) treatment • Goals of randomization: • Prevent bias • Balance the groups on variables that you know affect the response • Balance the groups on lurking variables that may be unknown to you

  6. Blinding the Study • Subjects should not know which group they have been assigned to – the primary treatment group or the control group • Data collectors and experimenters should also be blind to treatment information

  7. Example: A Study to Assess Antidepressants for Quitting Smoking • Design: • 429 men and women • Subjects had smoked 15 cigarettes or more per day for the previous year • Subjects were highly motivated to quit

  8. Example: A Study to Assess Antidepressants for Quitting Smoking • Subjects were randomly assigned to one of two groups: • One group took an antidepressant daily • Second group did not take the antidepressant (this group is called the placebo group)

  9. Example: A Study to Assess Antidepressants for Quitting Smoking • The study ran for one year • At the end of the year, the study observed whether each subject had successfully abstained from smoking or had relapsed

  10. Example: A Study to Assess Antidepressants for Quitting Smoking • Results after 1 year: • Treatment Group: 55.1% were not smoking • Placebo Group: 42.3% were not smoking • Results after 18 months: • Antidepressant Group: 47.7% not smoking • Placebo Group: 37.7% not smoking • Results after 2 years: • Antidepressant Group: 41.6% not smoking • Placebo Group: 40% not smoking

  11. Example: A Study to Assess Antidepressants for Quitting Smoking • Question to Think About: Are the differences between the two groups statistically significant or are these differences due to ordinary variation?

  12. Section 4.4 What Are Other Ways to Conduct Experimental and Observational Studies?

  13. Multifactor Experiments • Multifactor Experiments: have more than one categorical explanatory variable (called a factor).

  14. Example: Do Antidepressants and/or Nicotine Patches Help Smokers Quit?

  15. Matched-Pairs Design • Each subject serves as a block • Both treatments are observed for each subject

  16. Example: A Study to Compare an Oral Drug with a Placebo for Treating Migraine Headaches Subject Drug Placebo

  17. Blocks and Block Designs • Block: collection of experimental units that have the same (or similar) values on a key variable • Block Design: identifies blocks before the start of the experiment and assigns subjects to treatments with in those blocks

  18. Experiments vs Observational Studies • An Experiment can measure cause and effect • An observational study can yield useful information when an experiment is not practical • An observational study is a practical way of answering questions that do not involve trying to establish causality

  19. Observational Studies • A well-designed and informative observational study can give the researcher very useful data. • Sample surveys that select subjects randomly are good examples of observational studies.

  20. Random Sampling Schemes • Simple Random Sample: every possible sample has the same chance of selection

  21. Random Sampling Schemes • Cluster Random Sample: • Divide the population into a large number of clusters • Select a sample random sample of the clusters • Use the subjects in those clusters as the sample

  22. Random Sampling Schemes • Stratified Random Sample: • Divide the population into separate groups, called strata • Select asimple random sample from each strata

  23. Observational Studies • Well-designed observational studies use random sampling schemes

  24. Retrospective and Prospective Studies • Retrospective study: looks into the past • Prospectivestudy: follows its subjects into the future

  25. Case-Control Study • A case-control study is an observational study in which subjects who have a response outcome of interest (the cases) and subjects who have the other response outcome (the controls) are compared on an explanatory variable

  26. Example: Case-Control Study • Response outcome of interest: Lung cancer • The cases have lung cancer • The controls did not have lung cancer • The two groups were compared on the explanatory variable: • Whether the subject had been a smoker

More Related