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Population Sample Data, Binomial & Normal Distributions

1. Why do we want to assume that our sample data represent a population
distribution?

2. What are the differences between the binomial and normal distributions?

Thank you.

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PLEASE SEE RESPONSE ATTACHED (ALSO PRESENTED BELOW), INCLUDING ONE SUPPORTING ARTICLE. I HOPE THIS HELPS AND TAKE CARE.

RESPONSE:

1. Why do we want to assume that our sample data represent a population distribution?

We want to assume our sample represents a population distribution because we want to generalize our findings to the population. For example, if we researched a sample (N=30) of organizations and found that the team approach to management is more effective than authoritarian management style, we would want to conclude that this is true for all organizations (i.e., population). In order to do this, we must assume that our sample (N=30) is representative of the population of all organizations.

2. What are the differences between the binomial and normal distributions?

The main differences between the two distributions are in their location and scale parameters (mean and standard deviations) and in the overall shape of the distribution (bell-shaped versus not bell-shaped). Let's take a closer look at the two distributions.

a. Normal distributions are symmetrical and have a bell-shaped distribution.

The normal distribution, also called Gaussian distribution, is an extremely important probability distribution in many fields. It is a ...

Solution Summary

This solution discusses normal distribution and why we want to assume that our sample data represent a population distribution. The differences between the binomial and normal distributions are also discussed. Many examples are provided as well as one supporting article for further reading.

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