The Prices Of All New Cars At A Local Car Dealership Were Recorded. The Mean Price Was $47,244. Which Of The Following Describes The Value Of $47,244?A) Sample B) Statistic C) Population D) Parameter E) Census

The Value of a Mean Price: Understanding Statistics and Populations

When analyzing data, it's essential to understand the difference between a sample and a population, as well as the distinction between a statistic and a parameter. In this article, we'll explore the concept of a mean price and determine which of the given options best describes the value of $47,244.

What is a Sample?

A sample is a subset of data selected from a larger population. It's a representative group of individuals or items that are used to make inferences about the entire population. Samples are often used in statistical analysis because they're more manageable and cost-effective than collecting data from the entire population.

What is a Statistic?

A statistic is a numerical value that describes a sample. It's a characteristic of the sample, such as the mean, median, or mode. Statistics are used to summarize and describe the sample data, making it easier to understand and analyze.

What is a Population?

A population is the entire group of individuals or items that a sample is drawn from. It's the entire set of data that we're trying to make inferences about. Populations can be finite or infinite, and they can be defined by various characteristics, such as age, location, or occupation.

What is a Parameter?

A parameter is a numerical value that describes a population. It's a characteristic of the population, such as the mean, median, or mode. Parameters are used to describe the population, but they're often unknown and must be estimated using sample data.

What is a Census?

A census is a complete count of a population. It's a collection of data from every individual or item in the population, rather than a sample. Censuses are often used in government and research to gather accurate and comprehensive data.

Determining the Value of $47,244

Given that the mean price of all new cars at a local car dealership was $47,244, we need to determine which of the options best describes this value. Since the mean price is a characteristic of the sample (the group of new cars at the dealership), it's a statistic. However, the mean price is also an estimate of the population parameter (the mean price of all new cars). Therefore, the value of $47,244 is both a statistic and an estimate of a parameter.

In conclusion, the value of $47,244 is a statistic because it's a characteristic of the sample (the group of new cars at the dealership). However, it's also an estimate of a parameter because it's an attempt to describe the population (all new cars). Therefore, the correct answer is B) Statistic.

Additional Considerations

• If the mean price of all new cars at the dealership was calculated from a random sample of 100 cars, then the value of $47,244 would be a statistic.
• If the mean price of all new cars at the dealership was calculated from a complete count of all 500 cars, then the value of $47,244 would be a parameter.
• If the mean price of all new cars at the dealership was calculated from a survey of 100 car dealerships, then the value of $47,244 would be an estimate of a parameter.

Key Takeaways

• A sample is a subset of data selected from a larger population.
• A statistic is a numerical value that describes a sample.
• A population is the entire group of individuals or items that a sample is drawn from.
• A parameter is a numerical value that describes a population.
• A census is a complete count of a population.

By understanding the concepts of samples, statistics, populations, parameters, and censuses, we can better analyze and interpret data, making informed decisions in various fields, including business, research, and government.
The Value of a Mean Price: Understanding Statistics and Populations - Q&A

In our previous article, we explored the concept of a mean price and determined which of the given options best describes the value of $47,244. We also discussed the differences between a sample and a population, as well as the distinction between a statistic and a parameter. In this article, we'll answer some frequently asked questions related to statistics and populations.

Q: What is the difference between a sample and a population?

A: A sample is a subset of data selected from a larger population. It's a representative group of individuals or items that are used to make inferences about the entire population. A population, on the other hand, is the entire group of individuals or items that a sample is drawn from.

Q: What is a statistic, and how is it different from a parameter?

A: A statistic is a numerical value that describes a sample. It's a characteristic of the sample, such as the mean, median, or mode. A parameter, on the other hand, is a numerical value that describes a population. It's a characteristic of the population, such as the mean, median, or mode.

Q: What is the purpose of a census?

A: A census is a complete count of a population. It's a collection of data from every individual or item in the population, rather than a sample. Censuses are often used in government and research to gather accurate and comprehensive data.

Q: How do I determine whether a value is a statistic or a parameter?

A: To determine whether a value is a statistic or a parameter, ask yourself the following questions:

• Is the value describing a sample or a population?
• Is the value an estimate or a characteristic of the sample or population?

If the value is describing a sample, it's a statistic. If the value is describing a population, it's a parameter.

Q: What is the difference between a random sample and a non-random sample?

A: A random sample is a subset of data selected from a larger population using a random process. It's a representative group of individuals or items that are used to make inferences about the entire population. A non-random sample, on the other hand, is a subset of data selected from a larger population using a non-random process. It may not be representative of the entire population.

Q: How do I ensure that my sample is representative of the population?

A: To ensure that your sample is representative of the population, follow these steps:

• Define the population and the sample size.
• Use a random process to select the sample.
• Ensure that the sample is diverse and representative of the population.
• Use statistical methods to analyze the sample data and make inferences about the population.

Q: What are some common types of biases that can occur in sampling?

A: Some common types of biases that can occur in sampling include:

• Selection bias: This occurs when the sample is not representative of the population.
• Non-response bias: This occurs when some individuals or items in the population do not respond to the survey or data collection.
• Measurement bias: This occurs when the data collection method is flawed or biased.

In conclusion, understanding the concepts of samples, statistics, populations, parameters, and censuses is essential for making informed decisions in various fields, including business, research, and government. By following the steps outlined in this article, you can ensure that your sample is representative of the population and that your data analysis is accurate and reliable.

Additional Resources

• National Institute of Standards and Technology (NIST) - Statistics and Probability
• American Statistical Association (ASA) - Statistics and Probability
• World Health Organization (WHO) - Statistics and Data Analysis

Key Takeaways

• A sample is a subset of data selected from a larger population.
• A statistic is a numerical value that describes a sample.
• A population is the entire group of individuals or items that a sample is drawn from.
• A parameter is a numerical value that describes a population.
• A census is a complete count of a population.
• A random sample is a subset of data selected from a larger population using a random process.
• A non-random sample is a subset of data selected from a larger population using a non-random process.