Designing a Statistical Study Identify the variables of interest the focus and the population of the study Develop a detailed plan for collecting data Make sure sample is part of the population ID: 707864
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Slide1
Experimental Design
Section 1.3Slide2
Designing a Statistical Study
Identify the variable(s) of interest (the focus) and the population of the study.
Develop a detailed plan for collecting data. Make sure sample is part of the population.
Collect the Data
Describe the data, using descriptive statistic techniques.
Interpret the data and make decisions about the population using inferential statistics.
Identify any possible errors.Slide3
Ways to Collect Data
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)
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.Slide4
Ways to Collect Data
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.
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.Slide5
Examples:
A study of the effect of changing flight patterns on the number of airplane accidents.
Simulation
A study of the effect of eating oatmeal on lowering blood pressure.
Experiment
A study of how fourth grade students solve a puzzle
Observation
A study of U.S. residents’ approval rating of U.S. president
S
urveySlide6
Three Key Elements of a well designed experiment are:
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.Slide7
Three Key Elements of a well designed experiment are:
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.Slide8
Three Key Elements of a well designed experiment are:
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 20-29 year olds, 30-39, and 40-49. Then in those blocks randomly pick people to be in the treatment group or control group.Slide9
Three Key Elements of a well designed experiment are:
3.) Replacement
The repetition of an experiment using a large group of subjects.
HAVE LARGE SAMPLE SIZESSlide10
Placebo Effect
Placebo
– a faux treatment that looks like the real treatment (i.e. sugar pill
). It acts as a control.
Placebo Effect
– occurs when an untreated subject incorrectly believes that he/she is receiving a treatment and reports an improvement in symptoms.Slide11
Example:
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.Slide12
Example:
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 groupSlide13
Sampling TechniquesSlide14
Sampling Techniques
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 populationSlide15
5
Sampling Techniques
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 groupSlide16
5
Sampling Techniques
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 levelsSlide17
5
Sampling Techniques
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 coursesSlide18
5
Sampling Techniques
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.Slide19
5
Sampling Technique
Convenience Sample
Only use the available members of the population
Not recommended!!
You get biased resultsSlide20
Example
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 SampleSlide21
Homework
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