Type I and type II errors

the critical region) would cause us to reject the null-hypothesis in favor of the alternative hypothesis. The area of the rejection region is known as the level of significance and represents type I error (alpha) corresponding to the probability that a true null-hypothesis is rejected (as opposed to type II error- beta; the probability of accepting a false null-hypothesis). Essentially, hypothesis testing calls for comparing a test statistic to the critical value of the test statistic. If this test

BES Tutorial Sample Solutions, S1/13 WEEK 9 TUTORIAL EXERCISES (To be discussed in the week starting May 6) 1. Perform the following hypothesis tests of the population mean. In each case, illustrate the rejection regions on both the Z and ̅ distributions, and calculate the p-value (prob-value) of the test. (a) H0: μ = 50, H1: μ > 50, n = 100, ̅ = 55, σ = 10, α = 0.05 Rejection region: ̅ 50 1.645 . 10⁄√100 Alternatively 10 51.645 50 1.645 ̅ ̅ . √ √100 Since 55 50 5 1.645 .

The question addressed is if the airline Southwest meets or exceeds the national rate for on-time performance at the Oklahoma City airport. The data collected from the BTS is categorized into either on-time or not on-time for Oklahoma City and nationwide. The BTS data was a complete collection from participating airlines, which consist of airlines with 1 percent or more of scheduled domestic passenger revenues. A Chi-Squared Goodness-of-Fit test was selected for use. The Chi-Squared Goodness-of-Fit

Whereas, if I fail to reject the null, because I believe the groups are the same when, in fact, they are different this is a type 2 error. In its simplest terms, Type I error is the incorrect rejection of the null hypothesis, whereas Type II is the incorrect acceptance of the null hypothesis. Related to hypothesis testing is the Significance level or cut off point (alpha). The

HCS/438 DQ’s Week 1: DQ1: What are the differences between descriptive and inferential statistics? According to Bennett (2009), the biggest difference between descriptive and inferential statistics is that descriptive statistics "deals with describing raw data in the form of graphics and sample of statistics" and inferential statistics "deals with estimating population parameters from sample data." This means that inferential statistics would be an estimate because the data would be estimated

This discrepancy may increase the risk of type I or type II errors that may have an impact on public health as well as may cause economic loss due to the estimated 24 hour delay in reporting results. A type I error is the incorrect rejection of a true null hypothesis or in other words, it leads one to believe that a supposed effect or relationship exists when, in fact, it doesn’t (Banerjee et al., 2009). An example of this in terms of water quality monitoring is prohibiting swimming unnecessarily

Statistics is well-defined as a division of mathematics and is most often used to explain, analyze, and summarize as well as interpret what we perceive- in order to make sense or meaning of our interpretations. Every day we encounter statistics that originate from various forms and behaviors. Which means that to in order for all of the information gathered to make sense, there would realistically be no need for the use of statistics. Conversely, due to its focus and pragmatic on applications, statistics

Conceptual Framework The theoretical framework guiding this study conceptualizes policy analysis as a multidisciplinary inquiry aimed at the creation, critical assessment and communication of policy-relevant information. Policy analysis is a problem-solving discipline. To solve practical problems, the analysis draws on social science methods and theories (Dunn, 2012). The methodology of policy analysis is a process of “reasoned inquiry aimed at finding solutions to practical problems” (Dunn, 2012

| | |[pic]|d. Type II error [pic] | | Correct Marks for this submission: 1/1. Question 7 Marks: 1 If α = 0.05, what is the probability of making a Type I error? Choose one answer. |[pic]|a. 20/20 [pic] | | |[pic]|b. 19/20 [pic] | | |[pic]|c. 1/20 [pic] | | |[pic]|d. 0 [pic] | | Correct Marks for this submission: 1/1. Question 8 Marks: 1 What is the level of significance? Choose one answer. |[pic]|a. Beta error [pic]

Sheila Elyse Brooks Stanbridge College April 27, 2015 Introduction As we continue our journey through the "trials and errors" of understanding basic statistical terminology. Let 's focus our attention on the following: understanding what is the difference between a hypothesis, statistical hypothesis, and an experimental hypothesis. In addition, I want to explore how researchers determine the appropriate sample size. Now, some of you might be asking the question "what do we do