Selecting a data collection method requires matching the statistical question and variables to a census, sample survey, observation, or experiment, while distinguishing categorical from numerical data and identifying the population of interest. A random, representative sample supports generalization to the population, whereas biased sampling or leading questions can distort results; observational studies describe relationships without establishing cause, while well-designed experiments can provide evidence of causal effects. Formal sampling theory and advanced inference are beyond this level.
A good data collection method depends on:
Common methods include:
Question: “What proportion of students at a middle school bring a reusable water bottle?”
Step 1: Identify the population.
The population is all students at the middle school.
Step 2: Identify the variable.
The variable is whether each student brings a reusable water bottle. This is categorical, with choices such as “yes” or “no.”
Step 3: Choose a method.
A sample survey is appropriate. We can randomly select students and ask each one the same neutral question:
“Did you bring a reusable water bottle to school today?”
A survey is better than an experiment because we are only finding out what students do; we are not trying to change their behavior.
Step 4: Make the sample representative.
Select students randomly from the entire school, including different grades and groups. For example, choosing only students from the environmental club would be biased because they may be more likely to bring reusable bottles.
Step 5: Use the results.
If the random sample is representative, the proportion from the sample can be used to estimate the proportion for the whole school.
A census would also give information about the whole school, but it would take more time because every student would need to answer. An observation could record which students carry reusable bottles, but some bottles might be hidden in backpacks, making the data less accurate.
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