This module focuses on inferential statistics. As a reminder, inferential statistics are used to...

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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, XXXXXXXXXXThe 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.     I need 2-3 paragraphs, APA, 2-3 references.   Norman, G., and Streiner, D. (2008). Biostatistics the bare essentials (3rd ed.). BC Decker Inc. PMPH USA, Ltd. Shelton, CT. eISBN: XXXXXXXXXXpISBN: XXXXXXXXXX.
	
Document Preview: 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, XXXXXXXXXXAs 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 hypothesis.A Health Scientist may carry out an experiment to attempt to test a particular null hypothesis, so that it cannot be rejected unless the evidence against it is sufficiently strong. For example,Ho: there is no difference in likelihood of heart attack between patients who took Medication A compared to those who took Medication BH1: there is a difference in likelihood of heart attack between patients who took Medication A compared to those who took Medication BOne of the most important concepts to grasp in this course is the term “Significance”. Significant (in the statistical sense) means the likelihood of a particular result is probably not due to chance.In the example above, we estimate the probability of getting the observed data assuming that the null hypothesis is true. One useful statistic commonly used across disciplines is the p-value.The p-value may be defined as the probability of getting the observed result, or one more extreme, given that the null hypothesis is true. Researchers commonly choose in advance (i.e. a...

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RUNNING TITLE: INFERENTIAL STATISTICS
1
Inferential Statistics
7
Application of Inferential statistics to a Heath study
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Abstract
 
Inferential statistics is about testing hypotheses for determining whether the study results are not occurring at a rate that is likely to be due to chance (i.e., having statistical significance. Before testing of hypothesis, we must determine whether results are statistically significant and there is a probability of finding dissimilar results and also if the difference occurs purely by chance (-.05).  “In statistical testing process, all possible outcomes are covered by two predictions. These are either the result will be significant or not significant” (Tanner & Youssef-Morgan, 2013).  Jeff Gill talks about these outcomes as such, “an alternative hypothesis (H1) always competes with a null or restricted hypothesis (H0). The hypothesis is the model of probability describing the underlying data aspect, through a parameter: θ. Taking the simplest null hypothesis case, which asserts that θ = 0 (result is not significant), there is a competing and a complementary hypothesis asserts that θ ≠ 0.
Health study example
A recent health study undertaken estimated that 20% college students in the US smoke. The Health Services Head at Goodheart University (GU) however, suspects that GU has a lower proportion of smokers. In order to confirm her claim, Health Services Head picks a random sample consisting of 400 GU students and after analysing the results, she finds that out of 400 students chosen, 70 of them are smokers.
Let’s analyse this example
Stating the claims: There are two claims presented here:
Claim-1 represents proportion of smokers at Goodheart university is 0.20.
Claim-2 represents proportion of smokers at Goodheart university is lesser than 0.20.
Claim-1 fundamentally says that Goodheart University is no different as the proportion of smokers at Goodheart University is similar and no different from the proportion of smokers in the whole of US.” However, Health services head at Goodheart university has challenged this claim, who believes that GU has a lower proportion of smokers compared to the rest of the country.
Sample selection and data collection:
A sample n = 400 was chosen, and after summarizing the data, results revealed that the sample proportion of smokers is p-hat = 70/400 = 0.175. Though, it is true that the figure of 0.

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 &...

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