1.3 Experimental Design What is the point of a statistical study? Is the way you design the study important when reaching conclusions or making decisions? Designing a Statistical Study: 1. Identify the variable(s) of interest and the population of the study. 2. Develop a detailed plan for collecting data. The data must be representative of the population. 3. Collect data. 4. Describe the data with descriptive statistics. 5. Make decisions using inferential statistics. Identify any possible errors.

Let's talk about #3, collecting data... There are 4 popular ways to collect data: 1. perform an experiment - apply a treatment to part of the population and observe the responses. Use of a control group, another part of the population that is given no treatment or a placebo, is very important in an experiment. * the experimental and control groups must be similar! 2. perform a simulation - using technology or a physical model to reproduce the conditions of a situation or process. *Simulations save time and money! 3. take a census - count/measure an entire population. * costly and difficult 4. Use sampling - count/measure a part of the population then make predictions about the population. *more practical

A census and sampling both often use surveys, an ivestigation of one or more characteristics of a population. * are there any problems/bias with surveys? Example: What method of data collection would you use? 1. A study of the affect of an asteroid colliding with earth. 2. A study of the approval rating of Barack Obama. 3. A study of the heights of all centers in the NBA. 4. A study of the effect of nicoderm CQ on smokers trying to quit. * try it yourself 1

Now lets talk about sampling techniques... * we do not want biased samples! 1. simple random sample - every member of the population has an equal chance of being selected  number every member of the population and use a random number table ( or your calc.) to select who is in the sample. *Try it yourself 2 2. stratified sample - separate the population into groups, called strata, then do a simple random sample for each strata. e.g. separate the population into different age groups to insure that you have people of all ages in your sample. 3. cluster sample - the sample is a naturally occur subgroup. e.g. there are 7 statistics classes, 7 subgroups, so we can just pick of the classes to be the sample.

4. systematic sample - number the population, randomly select the first, then pick every kth (every 3rd, 5th, 10th, etc.) 5. convenience sample - whoever is available; leads to bias! * great overview on p. 18 & 19 * Try it yourself 3