- Select a simple random sample to estimate a population parameter
- Identify different sampling methods used select a random sample
- Recognize bias in sampling
Sampling Methods
The Main Idea
You learned about 4 different sampling methods in this section.
- Simple random sampling assigns a number to every member of the population, then uses a random number generator to select a sample.
- Systematic sampling assigns a number to every member of the population, then chooses individuals or entities from the population at regular intervals (e.g., every 4th individual from a randomly selected starting point).
- Stratified sampling divides a population into groups via some criterion, then uses simple random selection or systematic selection to collect a sample from each group.
- Cluster sampling divides a population into groups via some criterion, then uses simple random selection or systematic selection to select one or more groups as the sample.
- Convenience sampling selects a sample most accessible to the researcher. This is a biased method of sampling.
The video below discusses sampling bias and sampling methods.
Biased Sampling Methods
The Main Idea
Non-random sampling methods can exclude certain members of a population from having a chance to be selected in the sample. A sampling method like this is called a biased sampling method because it will tend to favor certain population groups over others.
You learned about 4 kinds of sampling bias in this section.
- Undercoverage occurs when some groups of the population are left out of the sampling process.
- Non-response bias occurs when an individual chosen for a sample cannot be contacted or decides not to participate in the study or research.
- Response bias occurs when a person does not understand a question or feels influenced to respond to a question in a certain way.
- Voluntary response bias occurs when the people who volunteer for a study or survey may be more inclined to respond to questions or report certain behaviors.
Random Sampling
The Main Idea
Sampling methods that are not statistically random are biased. That is, they inherently exclude certain members of the population from being selected in the sample.
Unbiased sampling produces samples that are representative of the population.
Representative samples can be used to generalize from the sample to the population.
Random sampling methods eliminate bias by removing human opinion or preference from the sampling method.
Simple random sampling is a method of random sampling in which every member of the population has an equal chance of being selected in the sample. We can assign a unique number to each population member and use a random number generator to collect random samples.
Selection of a Sample
Of course, we would like a sample that represents the population well. The word random is often used in the selection of the sample. Random, used statistically, means that each individual in the population has the same chance of being selected in the sample. The use of cards in the question above involves the idea of random-ness. Remember that we should always anticipate variability from one sample to another.
There are multiple avenues that can be utilized to select a random sample, such as a random number generator.
STEP 1: Under “Choose Minimum,” select “1.”
STEP 2: Under “Choose Maximum,” select “50.”
STEP 3: Under “How many numbers do you want to generate,” select “6.”
STEP 4: Under “Sample with Replacement,” select “No.”
STEP 5: Click “Generate.” This will generate six random numbers between 1 and 50.
Save these six numbers for the next question.
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Write down on a piece of paper your sample of the six states and the number of drivers involved in fatal collisions per billion miles for each of your randomly-generated numbers.