Sampling Eye Color Based on a study by Dr. P. Sorita Soni at Indiana University, assume that eye colors in the United States are distributed as follows: 40% brown, 35% blue, 12% green, 7% gray, 6% hazel.


a. A statistics instructor collects eye color data from her students. What is the name for this type of sample?

Verified step by step guidance

1. Identify the type of sample being collected. In this scenario, the statistics instructor is collecting eye color data from her students.
2. Understand that the sample is not randomly selected from the entire population of the United States, but rather from a specific group (students in the instructor's class).
3. Recognize that this type of sample is known as a 'convenience sample'. A convenience sample is one where the sample is taken from a group that is easy to access or contact.
4. Consider the implications of using a convenience sample. It may not be representative of the entire population, as it is limited to a specific subset (students in the class).
5. Reflect on how the results from a convenience sample might differ from those obtained from a random sample of the entire population, potentially leading to biased or non-generalizable conclusions.

Key Concepts

Sampling

Sampling is the process of selecting a subset of individuals from a population to estimate characteristics of the whole population. It is crucial in statistics to make inferences about a population without examining every individual. Different sampling methods can affect the accuracy and reliability of the results.

Convenience Sampling

Convenience sampling is a non-probability sampling technique where samples are selected based on their easy availability and proximity to the researcher. This method is often used in educational settings, like collecting data from students, but it may not represent the broader population accurately due to potential biases.

Population Distribution

Population distribution refers to the way in which different characteristics, such as eye color, are spread across a population. Understanding the distribution helps in comparing sample data to the expected proportions in the population, which is essential for assessing representativeness and potential biases in sampling.