Random Sampling
Definition
Random sampling is a method of selecting individuals from a population in such a way that every individual has an equal chance of being chosen. It helps to ensure that the sample represents the population accurately.
Analogy
Imagine you have a bag of different colored candies, and you want to know what percentage of each color is in the bag. To get an accurate representation, you close your eyes and randomly pick out a handful of candies. This way, each candy has an equal chance of being selected, just like random sampling ensures each individual in a population has an equal chance of being chosen for a sample.
Related terms
Simple Random Sample: A simple random sample is when every possible sample size has an equal chance of being selected from the population.
Stratified Sampling: Stratified sampling involves dividing the population into subgroups (strata) based on certain characteristics and then randomly selecting samples from each subgroup.
Cluster Sampling: Cluster sampling involves dividing the population into clusters or groups and then randomly selecting entire clusters to be included in the sample.
"Random Sampling" appears in:
Study guides (7)
AP Statistics - 3.1 Introducing Statistics: Do the Data We Collected Tell the Truth?
AP Statistics - 3.3 Random Sampling and Data Collection
AP Statistics - 3.6 Selecting an Experimental Design
AP Statistics - 5.3 The Central Limit Theorem
AP Statistics - 6.8 Confidence Intervals for the Difference of Two Proportions
AP Statistics - 7.8 Setting Up a Test for the Difference of Two Population Means
AP Statistics - 7.10 Skills Focus: Selecting, Implementing, and Communicating Inference Procedures
Additional resources (4)
Practice Questions (2)
- Why is random sampling important in two-sample z-intervals?
- Under what conditions can independence be assumed in a two-sample t-test without random sampling?
Resources
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