Post a reading report on this discussion board forum answering the following questions:
1. What are the differences between descriptive and inferential statistics?
Descriptive stats summarize data so the data can be comprehended. The researchers prepare a frequency distribution which shows the frequencies as descriptive statistics. Percentages, and averages are also descriptive statistics. Therefore, the descriptive statistics describe sets of data collected through observation. Then the statistics are organized in tables, pie charts, graphs etc. Researchers must be sure the kind of descriptive statistics matches the kind of data that has been collected.
Influential statistics is when, due to the size of the group that needs to be studied, the researchers limit the size by doing random sampling. Random sampling produces random errors called sampling errors The purpose of this type of data is to help draw inferences about the effects of those sampling errors described in the descriptive statistics. Thus, the inferential data helps researchers generalize, keeping in mind that there will be margins of error, and they can also evaluate results in light of sampling errors. Consequently, if no sampling is done, inferential statistics are not done. Inferential statistics does help researchers decide whether the difference in descriptive statistics are reliable (Earls and Anderson, 2015).
2. What is the null hypothesis? Why is the null hypothesis important?
A null hypothesis is when samples are taken in inferential statistics but those samples are unrepresentative because of random sampling errors. This can happen in three ways: 1. The observed difference wa...
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...s then compared to a normal distribution by measuring the magnitude of differences between two sets of standardized data so they can both be on the same scale. Therefore, this effect size refers to the magnitude of the difference when it is expressed in the standardized scale. This statistic d is a popular stat to describe the difference between two means (Patten, p. 146, 2014). Finally it is a standardized difference. For example, the ratio of the difference between the means to standard deviation. Therefore, the meta-analysis and effect size go hand in hand in computing effect sizes.
Earls, J., & Anderson, K. (2015). Lecture on sampling. Personal Collection of J. Earls and K. Anderson, University of Nebraska at Kearney, Kearney, NE.
Patten, M. L. (2014). Understanding research methods: An overview of the essentials (9th ed.). Glendale, CA: Pryczak.
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