1. Identify the variable(s) of interest (the focus) and the population of the study. 2. Develop a detailed plan for collecting data. Make sure sample is part of the population. 3. Collect the Data 4. Describe the data, using descriptive statistic techniques. 5. Interpret the data and make decisions about the population using inferential statistics. 6. Identify any possible errors.

1. Observational Study › Researcher observes and measures but does not change the environment at all.  Example: Researches observed and recorded what children up to three years old did with nonfood objects (saw if they put it in their mouths) 2. Experiment › Treatment applied to part of a population and responses are observed. You can also use a control group and a placebo.  Example: Diabetics take a pill to see if helps reduce their risk of heart disease while a control group took a water pill.

3. Simulation › Mathematical or physical model used to reproduce the conditions of a situation › Done when experiment is too dangerous or costly.  Example: Automobiles use dummies when they are studying the effects of crashes on humans. 4. Survey › Investigation of one or more characteristics of a population (interview, mail, telephone)  Example: A survey conducted on females physicians to determine whether the primary reason for their career choice is financial stability.

1. A study of the effect of changing flight patterns on the number of airplane accidents. › Simulation 2. A study of the effect of eating oatmeal on lowering blood pressure. › Experiment 3. A study of how fourth grade students solve a puzzle › Observation 4. A study of U.S. residents’ approval rating of U.S. president › Survey

 1.) Control influential factors › A confounding variable occurs when an experimental cannot tell the difference between the effects of different factors on a variable.  Example: A coffee shop owner wants to attract more customers into her shop so she decorates it in bright colors. At the same time a new shopping mall opens up. If the business at the shopping mall increases you can not determine if it is the new colors or the shopping mall.

› Placebo Effect  occurs when a subject acting favorable to a placebo even when they received no medication.  Example: Someone who has depression is given medicine which in fact is a water pill. The person then starts to feel better because they believe the medicine is working.

 2.) Randomization – Randomly assign subjects to different treatment groups › Could have groups being completely random. › Could have groups be in blocks  Blocks are groups of subjects have the same characteristics › Could have groups be in a randomized block design  Example: An experiment of a weight loss drink. You may create blocks of year olds, 30-39, and Then in those blocks randomly pick people to be in the treatment group or control group.

 3.) Replacement › The repetition of an experiment using a large group of subjects. › HAVE LARGE SAMPLE SIZES

 The company identifies ten adults who are heavy smokers. Five of the subjects are given the new gum and the other five subjects are given a placebo. After two months, the subjects are evaluated and it is found that the five subjects using the new gum have quit smoking. › Sample size too small, should be replicated. › Results of the 5 adults who were given the placebo are not given.

 The company identifies 1,000 adults who are heavy smokers. The subjects are divided into blocks according to their gender. Females are given the new gum and males are given the placebo. After 2 months, the female group has a significant number of subjects who have quit smoking. › Groups not similar. Divide into blocks and then split the blocks into treatment group and control group. › Don’t know the results of the men's group

 Census › A count or measure of an entire population (costly and difficult)  Sampling › A count or measure of part of a population  Sampling error › The difference between the results of a sample and those of a population

1. Random Sample › Every member of the population has the same chance of being selected a. Simple Random Sample › Every possible sample of the same size has the chance of being selected › Appendix B › Assign a different number to every member of the population and use of a random number generator to choose group

2. Stratified Sample › Used when it is important to have members from each segment of the population in our sample › Members of a population are divided into two or more subsets that are called strata that share a similar characteristic such as age, gender, ethnicity, etc. › A sample is randomly selected from each strata › Example: Divide homes into socioeconomic levels

3. Cluster Sample › Use when population falls into naturally occurring subgroups, each having similar characteristics › Divide population into groups called clusters › Select all members in one or more clusters (not all) › Example: Divide into zip codes, Class courses

4. Systematic Sample › Each member of the population is assigned a number › Members are ordered in some way › Starting number is selected, and then sample members are selected at regular intervals (every 3 rd, every 5 th, etc.) › Example: Assign numbers to each house in Cranberry Township and then select every 100 th household.

5. Convenience Sample › Only use the available members of the population › Not recommended!!  You get biased results

 There are 731 students currently enrolled in statistics in our school. You wish to form a sample of eight students to answer some survey questions. How would you select the students to be part of a simple random sample?  Have each house in Cranberry Township be given a number. You can use a random number generator to chose what houses to be selected as the sample.

 You select a class at random and question each student in the class. › Cluster  You divide the student population with respect to majors and randomly select and question some students in each major. › Stratified  You question every 20 th student you see in the hall. › Systematic  You assign each student a number and generate random numbers. You then question each student whose number is randomly selected. › Simple Random Sample

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