Statistical Study Design:

1. Identify individuals of interest2. Specify variables and protocols for measurements3. Decide on sampling method4. Collect the data5. Use appropriate descriptive and inferential techniques6. Identify possible errors

Data Collection:

CensusSamplingSimulationExperimentObservationSurvey

Measurements or observations of an entire population

Data Collection:

CensusSamplingSimulationExperimentObservationSurvey

Measurements or observations of a part of

a population

Data Collection:

CensusSamplingSimulationExperimentObservationSurvey

Use a mathematical or physical model to reproduce

a dangerous or difficult situation

Data Collection:

CensusSamplingSimulationExperimentObservationSurvey

Apply a treatment to part of a population, observe resulting change in variable of study.

Must be controlled

“Completely randomized experiment”

Placebo

Double blind

Data Collection:

CensusSamplingSimulationExperimentObservationSurvey

Observations and measurements are made in a way that will not change the variable of study.

Data Collection:

CensusSamplingSimulationExperimentObservationSurvey

Can be used for either a census or a sampling.

Can easily be biased...

Bias - When the sample doesn’t really represent the population

Are questions asked in a neutral way?“Don’t you think the President could be doing a better job?”

Will the location provide an appropriate cross-section?Conducting a survey on US military presence in 3rd world countries - outside the gates of an army base.

Is the surveyor neutral?Uniformed police officer surveying college students on drug use.

Lurking variablesA variable that is not measured in the study, but has some influence over the measured variables.

WW II bombing accuracy studies: - Higher altitude ↔ lower accuracy- Different bomber models have different accuracies

- The presence of enemy fighters ↔ higher accuracy

What’s the lurking variable?

Another example: # of firefighters at a fire ↔ damage done

Confounded variablesConfounded variables both vary in the study, and in a way that makes it difficult to tell which one is causing the effect.

Offer both a low interest rate and low fee to one group of customers and a higher interest rate and higher fee to another. You'll never be able to tell whether customers were more motivated by the difference in interest rate or the difference in fee.

Sampling Techniques:

Simple randomStratifiedSystematicClusterConvenience

Every possible sample of the same size has an

equal chance of being chosen

Sampling Techniques:

Population has multiple segments - each segment is represented proportionally

Simple randomStratifiedSystematicClusterConvenience

Sampling Techniques:

Sample every Nth member starting at some

random point

Simple randomStratifiedSystematicClusterConvenience

Sampling Techniques:

Population is broken into subgroups - survey all of one or more subgroups

Simple randomStratifiedSystematicClusterConvenience

Sampling Techniques:

Sample whatever members are easily at hand

Likely to be biased!

Simple randomStratifiedSystematicClusterConvenience