RUSH RESPONSE
Timesaver4real
Module4-StudentDiscussion.docxStudent 1
The most interesting part relating to normal distribution in chapter 5 was the value of the central limit theorem. According to Bennett et al (2018), polls and surveys used for statistical sampling allows the researcher to make a good estimate of the population mean by using only the mean and the standard deviation of the entire population. Figure 5.2 shows how “as the sample size increases, the distribution of sample means approaches a normal distribution, regardless of the shape of the original distribution” (p. 179). The book gave an example of a dice rolling experiment that proves this rule showing that the more times the dice was rolled (the larger sample size was), the closer the standard deviation of the mean, was to zero. Additionally, this rule makes any sample size over 30 (n>30) considered to be nearly normal in distribution, regardless of the population. I find this helpful for psychology research because although a researcher does not know which mean in the sampling distribution is the same as the population mean, they can select many random samples from a population, allowing for a good estimate of the population mean (McLeod, 2019). This is likely why data from larger sample sizes (above 30) are stronger more reliable.
References
Bennet, J., Briggs, W.L., & Triola, M.F., (2018). Statistical reasoning for everyday life (5th ed.). Pearson Education, Inc.
McLeod, S. A. (2019, Nov 25). What is central limit theorem in statistics? Simply psychology. https://www.simplypsychology.org/central-limit-theorem.html
Student 2
The most interesting part of Normal distribution is the "bell-shape" distribution. The term "bell-shape" which is commonly referred to as the Gaussian curve is named after the 19th century German mathematician Carl Friedrich Gauss, the American Logician Charles Peirce introduced the term normal distribution in 1870 (Briggs,2018). The normal distribution is the most important probability distribution in statistics because many continuous data in nature and psychology displays this bell-shaped curve when compiled and graphed (Mcleod,2019). When the data is symmetrical it produces a bell shape that is frequently seen in psychology. When there is a normal distribution, it can help bring better clarification to data that has been researched. The data that is calculated within a normal bell-shape allows for scientist to know where most of the data they are looking for lies. The bell shape distribution has been used to help to distinguish variations within testing and the scores such as in IQ tests and SAT scores.
Bennett, J. O., Briggs, W. L., & Triola, M. F. (2018). Statistical reasoning for everyday life. Boston: Pearson.
McLeod, S. A. (2019, May 28). Introduction to the normal distribution (bell curve). Simply psychology: https://www.simplypsychology.org/normal-distribution.html
