Random sampling and inference
Random Sampling and Inference
Random Sampling and Inference is a topic in Data & Graphs in the Texas Essential Knowledge and Skills. It is aligned to Standard 7.12(A), which requires students to use random sampling to make inferences about a population.
A random sample represents a population without bias, letting you infer population characteristics from the sample.
Theory
A random sample is a subset chosen so every member has an equal chance, making it representative.
The idea:
How to sample and infer
- Define the population.
- Choose a random, representative sample.
- Measure the sample.
- Infer the population, noting uncertainty.
To avoid bias and represent the population.
About \(60\%\) of the population likely prefer A.
Yes — it is not representative.
Yes — it gives a more reliable estimate.
Common pitfalls
Frequently asked questions
What is a random sample?
A subset where each member has an equal chance.
Why use one?
To avoid bias and represent the population.
What is inference?
Estimating a population from a sample.
What is a biased sample?
One that misrepresents the population.