## Assignment: Descriptive Statistics To Inferential Statistics

Assignment: Descriptive Statistics To Inferential Statistics
Assignment: Descriptive Statistics To Inferential Statistics
In this module, we shift gears from descriptive statistics to inferential statistics. Inferential statistics are used to determine the probability that a conclusion based on analysis of data from a sample is true (Norman & Streiner, 2008). As statisticians, we keep in mind that when gathering data on a sample of people there is a possibility for random error. In other words, measurements drawn at random from a population of individuals of interest will differ by some amount as a result of random processes.
We start by formulating a null hypothesis. A null hypothesis is an assumption that there is no significant difference between a sample mean and a population mean. We then formulate an alternate hypothesis that is mutually exclusive.
The primary goal of a statistical test is to determine whether an observed data set is sufficiently different from what we would expect under the null hypothesis that we should reject the null
This module focuses on inferential statistics. As a reminder, inferential statistics are used to determine the probability that a conclusion based on analysis of data from a sample is true (Norman & Streiner, 2008). The purpose of this discussion is to show the various types of hypotheses, how to identify them in an article and the importance of significance and a p-value.
For this discussion, use a peer-reviewed article (focused on a health study) of your choice to:
· Identify the Ho and H1
· Identify and explain what significance is in a general sense and in your chosen article. Be sure to discuss the p-value.
Answer must be at least 500 words
References:
Cook, A., Netuveli, G., & Sheikh, A. (2004). Chapter 4: Statistical inference. In Basic skills in statistics: A guide for healthcare professionals (pp. 40-52). London, GBR: Class Publishing. eISBN: 9781859591291.
Davis, R., & Mukamal, K. (2006). Statistical primer for cardiovascular research: Hypothesis testing. Circulation, 114(10), 1078-1082. Retrieved from
Johnson, L. (2008). Principles of hypothesis testing for public health. National Center for Complementary and Alternative Medicine. Retrieved from
Statistics Learning Centre. (2011, December 5). Hypothesis tests, p-value  Statistics help