key term - Random Sample

Definition

A random sample is a subset of a population that is selected in a way that ensures each member of the population has an equal chance of being chosen. This type of sampling is a fundamental concept in statistics, as it allows researchers to draw inferences about the larger population from the data collected on the sample.

5 Must Know Facts For Your Next Test

1. A random sample is an unbiased representation of the population, as each member has an equal chance of being selected.
2. Random sampling ensures that the sample is representative of the population, allowing for valid statistical inferences to be made.
3. Random sampling helps to minimize the impact of confounding variables and other sources of bias that can skew the results.
4. The size of the random sample is an important consideration, as larger samples are generally more representative of the population.
5. Random sampling is a key assumption for many statistical techniques, such as confidence interval estimation and hypothesis testing.

Review Questions

• Explain how a random sample is selected and why it is important in the context of confidence interval estimation for women's heights.
  • To estimate a confidence interval for the average height of women, a random sample of women would be selected from the population. This means that each woman in the population has an equal chance of being included in the sample. A random sample ensures that the sample is representative of the overall population of women, which is crucial for making valid statistical inferences about the true average height. By selecting a random sample, the researcher can be confident that the sample statistics, such as the sample mean, will be unbiased estimates of the corresponding population parameters.
• Describe the role of random sampling in the assumptions required for constructing a confidence interval for the mean height of women.
  • One of the key assumptions for constructing a confidence interval for the mean height of women is that the sample is a random sample from the population. This assumption ensures that the sample is representative of the population and that the sample statistics, such as the sample mean and standard deviation, can be used to make valid inferences about the population parameters. If the sample is not random, it may not be representative of the population, and the confidence interval may not accurately reflect the true population mean. Therefore, the use of a random sample is essential for meeting the assumptions required to construct a reliable and valid confidence interval for the mean height of women.
• Analyze the importance of random sampling in the context of making inferences about the population of women's heights based on a sample.
  • $$\text{The use of a random sample is crucial when making inferences about the population of women's heights.} \ \text{Random sampling ensures that the sample is an unbiased representation of the population, which allows}\text{the researcher to draw valid conclusions about the population parameters, such as the mean height.} \ \text{If the sample is not random, it may be biased and not accurately reflect the true characteristics of the population.} \ \text{This would lead to inaccurate estimates and invalid inferences about the population of women's heights.} \ \text{Therefore, the random sampling assumption is essential for the reliability and validity of any statistical}\text{analysis, including the construction of confidence intervals for the mean height of women.}$$

Related terms

Population:

The entire group of individuals or objects that a researcher is interested in studying.

Sampling:

The process of selecting a subset of a population to represent the whole.

Probability Sampling: A sampling method where each member of the population has a known, non-zero probability of being selected.