1. 1.3 Experimental Design Prob & Stats Mrs. O’Toole

2. Objectives  Learn how to design a statistical study  Learn how to collect data  Learn how to design an experiment  Learn how to create a sample

3. Design of a Statistical Study 1. Identify a variable of interest and the population of the study 2. Develop a plan for collecting data 3. Collect the data 4. Describe the data using statistics 5. Interpret the data and make decisions/conclusions about the population 6. Identify any possible errors and suggest topics for future study

4. Four methods of Data Colleciton 1. Observational study – researcher observes and records characteristics Example: Researchers observe and record the behavior of a group of chimpanzees

5. Four methods of Data Collection 2.Experiment – a treatment is applied to part of the population (control group is not given the treatment) Example: Diabetics took cinnamon extract daily while the control group took none. After 40 days, the diabetics who took the extract reduced their risk of heart disease, while the control group experienced no change.

6. Four methods of Data Collection 3. Simulation – a model is used to reproduce the conditions of a situation or process Example: Automobile manufacturers use simulations of car crashes with dummies to study the effects of car crashes on humans

7. Four methods of Data Collection 4. Survey – ask people questions in order to investigate one or more characteristics of a population Example: A survey of female physicians is conducted to determine whether the primary reason for their career choice is ‘financial stability’.

8. Example:Decide on the appropriate method for data collection. A study of the effect of exercise on relieving depression. Experiment A study of the success of graduates of a large university finding a job within one year of graduation. Survey

9. End of Day 1 Assignment: Read p. 18-24 Do p.25 (11-14)

10. 1.3 Experimental Design Day 2 Objectives:  Learn how to design an experiment  Learn how to create a sample

11. BEWARE!!!!!! There are many things that can render your experiment invalid or biased. The purpose of this lesson is to increase your awareness of those things, and to suggest strategies you can use in designing your experiment that will minimize your chances for bias.

12. Definitions  Confounding variable – occurs when you can’t tell the difference between the effects of different factors on a variable. Example: A coffee shop remodels its store front in order to attract more customers. At the same time, a shopping mall opens nearby. What is the increased business due to?

13. Definitions  Placebo effect – occurs when a subject reacts favorably to a placebo when if fact, he or she has been given no medicated treatment at all To avoid this, researchers use:  Blinding – a technique where the subject does not know whether he or she is receiving the treatment or the placebo

14. Definitions  Double-blind – neither the subjects nor the experimenter is aware of who receives the placebo  Randomization – a process of randomly assigning subjects to different treatment groups Example: A researcher studying the effects of a new weight loss drink first divides the subjects into age categories (such as 25-29 yrs old, 30- 34 yrs old, etc.). Within each age group, she randomly assigns subjects to the treatment group or the placebo group.

15. Definitions  Replication – the repetition of an experiment using a large group of subjects Example: An experiment is designed to test a vaccine against the flu. 10,000 people are given the vaccine, and 10,000 people are given a placebo.

16. Example  A company wants to test the effectiveness of a new gum developed to help people quit smoking.  240 adults who are heavy smokers are identified.  Subjects are randomly assigned to be in the treatment group or the control group.  Each subject is also shown a DVD featuring the dangers or smoking.  After 4 months, most of the subjects in the treatment group have quit smoking. Identify a potential problem with the experimental design. How could this design be improved?

17. Sampling Techniques  Random sample – every member of the population has an equal chance of being selected  Simple random sample – every possible sample of the same size has the same chance of being selected Example: Assign a number to each member of the population, then use a random number table (see p.22) to select members of the population to use in the sample.

18. How to use a Random Number Table Use the table below. 92630782401926795457 23894673189403276938 A company employs 79 people. Choose a simple random sample of 5 people to survey.

19. How to use a Random Number Table 92630782401926795457 23894673189403276938 1. Choose a place to start. 2. Read the digits in groups of two (for this problem, since there are 79 employees) 3. Throw out numbers that are too big 6307401926

20. Sampling Techniques  Stratified sample – for use when you need the sample to have members from every segment of the population Example: Freshmen, sophomores, juniors, seniors  Cluster sample – for when the population falls into naturally occurring subgroups, each having similar characteristics Example 1: different sections of the same course Example 2: different branches of the same bank

21. Sampling Techniques  Systematic sample – each member of the population is assigned a number, then (for instance) you use every 3 rd member Example: Every 3 rd house along a street  Convenience sample – (not recommended) consists only of available members of a population Example: Survey only the people in this classroom

22. Assignment p.25 (15, 16, 17-27, 31-34, 39-41)