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During the previous calendar year, a county's small claims court processed 870 cases. Describe how a simple random sample of size \(n=50\) might be selected from the case files to obtain information regarding the average award in such cases.

Short Answer

Expert verified
Assign unique numbers to each case and randomly generate 50 numbers between 1 and 870. Select the cases corresponding to the randomly generated numbers to form your sample. From this random sample, you can calculate statistical data like the average award.

Step by step solution

01

Number the cases

First, assign each of the 870 cases a unique number from 1 to 870. This is necessary for maintaining an organized selection of cases for the random sample.
02

Use a random number generator

Next, using an appropriate tool such a random number generator, generate 50 random numbers between 1 and 870. Ensure these numbers are not repeated. Use a computer program or statistical software to do this task.
03

Select the cases

Finally, select the cases based on the generated random numbers and use them to form the sample of size \(n=50\). This forms the random sample from the population of cases. From this sample, statistical analysis such as calculation of the average award can be conducted.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Random Number Generator
A Random Number Generator (RNG) is an essential tool in the process of creating a simple random sample. This tool generates a sequence of numbers that lack any discernible pattern, effectively simulating the randomness akin to drawing lots from a hat.

In our county's small claims court, an RNG would randomly select numbers assigned to case files.
  • Each case must be numbered from 1 to 870.
  • The RNG will select 50 random numbers from this range.
  • The primary goal here is to ensure each case has an equal chance of being chosen.

Using computer programs and statistical software like Excel or Python's random module simplifies this task. By ensuring numbers aren't repeated, the sample maintains its randomness, providing a true reflection of the population.

Random number generators help eliminate biases, which is crucial for the reliability of statistical conclusions drawn from the sample.
Population and Sample
In any statistical study, understanding the population and sample is fundamental. The population refers to the entire group about which we want to draw conclusions. In our scenario with the small claims court, the population comprises all 870 cases from the past year.

On the other hand, a sample is a smaller subset, selected from this larger group.
  • The sample size here is 50 cases.
  • By selecting a sample, researchers can gather insights without examining every single case.

Using a representative sample allows for efficient analysis, saving both time and resources. This method is often used when it’s impractical to study the whole population. The key is to ensure the sample accurately reflects the population to make valid inferences.
Statistical Analysis
Once a simple random sample is obtained, statistical analysis can commence. This process involves examining the data gathered from the sample to understand trends, patterns, or insights about the larger population.

In the context of our small claims court cases, one might want to calculate the average award given. Calculating this average from the sample serves as an estimate of the average in the entire population of cases.
  • Begin by adding all award amounts from the sampled cases.
  • Then, divide this total by the number of cases in the sample, which is 50 in this situation.
  • The result provides an estimate of the average award for all small claims processed.

Statistical analysis goes beyond just the mean, allowing for further exploration of variability, median values, and potential outliers within the sample. This scientific approach ensures decisions and conclusions made are backed by data rather than assumptions.

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