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Descriptive Statistics, Reliability and Validity

1. Reliability vs. Validity. Considering your area of research interest, discuss the importance of reliability and validity. Can you have one without the other? Why or why not?
2. Sample vs. Population. Considering your area of research interest, describe the difference between a sample and population. Why is it important to understand the difference between a sample and population in a statistics course?
3. Measures of Central Tendency. Below is a set of data that represent weight in pounds for a particular sample. Calculate the mean, median and mode. Which measure of central tendency best describes this data and why? You may use Excel, SPSS, some other software program, or a hand calculator for this problem.
110.00
117.00
120.00
118.00
104.00
100.00
107.00
115.00
115.00
115.00
114.00
100.00
117.00
115.00
103.00
105.00
110.00
115.00
250.00
275.00
4. Measures of Dispersion. For the data set above, calculate the range, the interquartile range, the variance, and the standard deviation. What do these measures tell you about the "spread" of the data?
5. Descriptive Statistics. Why is it important to perform basic descriptive statistics prior to conducting inferential statistical tests?Statistical Significance. Revisit the hypotheses you created above in #5. If you conducted a statistical test based on these hypotheses and found a statistically significant result, what would that mean from both a statistical and practical standpoint? (Be sure to use the phrases "null hypothesis" and "effect size" in your answer).Type I and Type II Error. The concept of Type I and Type II Error is critical and will come into play not only with each and every statistical test you perform, but when you are asked to conduct an a priori power analysis for your Dissertation Proposal. Considering your answer to #10, discuss the implications of making both a Type I and Type II error.

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1. Reliability vs. Validity. Considering your area of research interest, discuss the importance of reliability and validity. Can you have one without the other? Why or why not?
Reliability: the degree of producing stable and consistent results by using some tool is known as reliability.
Validity: if we have a tool to measure something with us then validity will tell how well the tool is.
From reliability we will come to know how much reliable the tool is which we are using in our research i.e. how much stable and consistent results are being produced by the tool. On the other hand, from validity we will come to know how well the tool is.
We cannot have one without other as in this research area it is very important to use both the reliability and validity because the tool which we are using is has to be reliable as well as valid. For the valid, stable and consistent results we will have both reliability and validity.
References:
https://www.uni.edu/chfasoa/reliabilityandvalidity.htm
http://www.nationaltechcenter.org/index.php/products/at-research-matters/validity/


2. Sample vs. Population. Considering your area of research interest, describe the difference between a sample and population. Why is it important to understand the difference between a sample and population in a statistics course?
Population consists of all data or collection of similar objects whereas sample consists of some data from the full data or group. Population is the complete group of the people on which we want to perform the research. We cannot take full data to perform the analysis so we always ...

Solution Summary

This solution is comprised of a detailed explanation on descriptive statistics, statistical tests, reported results and relationship between variables based on the cases studies. All questions were answered based on the data and information provided in the description of the problem. Also the explanation on appropriate statistic method used for the objectives defined in the problem.

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