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1. Respond to the post below. the significance level is a crucial…
1. Respond to the post below.
the significance level is a crucial component of hypothesis testing that determines the maximum probability of making a Type I error. Type I error represents a false positive result, while Type II error represents a false negative result. The choice of significance level and the balance between Type I and Type II errors depend on the specific problem and its consequences. Thus the significance level is a crucial component of hypothesis testing that determines the maximum probability of making a Type I error. Type I error represents a false positive result, while Type II error represents a false negative result.
2. Respond to the post below.
A fluke for me would be the direction my life took when we decided to start a farm. We were on such a different course; my husband had just graduated with mechanical engineering degree (hence why I don’t like math as I am married to a human calculator) and we had planned to move to California or maybe Dallas so he could work for a big company. In a blink of an eye we were purchasing 300 hundred acres and building our own home. I consider this a fluke because this life is not for everyone and we could’ve had such a different life.
3. Respond to the post below.
To set the criteria for the Decision (step 2 in hypothesis testing) the critical value must be identified as well as the rejection region. Most importantly, the “level of significance”, the sample sizes that are presented, and the direction of the test are all important for the rejection region criteria. The direction of the tests all derives on the alternative hypothesis. Mainly because a two-failed test contains the tails of the distribution both negative and positive. This means that it will examine both tests. Not like a one failed test which determines the difference between one direction.
4. Respond to the post below.
The sample values come from the null hypothesis and are necessary for the construction of the criteria for the decision. The important zone can be decided based on the alpha level. If the sample information falls inside the critical range, it is plausible to determine that the null hypothesis is incorrect. When the difference between the sample and the population is minute and the difference is in the desired direction, it is common practice to use a one-tail test to reject the null hypothesis. This is because such a test only considers the direction of the difference. The two-tail test is applied in scenarios when a research problem has two hypotheses that are in direct opposition to one another. In order to employ a test with two tails, there has to be a significant difference in either direction. In general, you should employ two-tailed tests unless there is a good reason for a directional prediction, which would be based on previous research or theory.
5. Respond to the post below.
Hi Torie, you are right the post hoc test is required when there is a statistically significant result that we need to determine where the differences came from. The post hoc tests are an important part of ANOVA, because ANOVA results do not identify which particular differences between pairs of means are significant, using the post hoc tests reveal this , and apart from revealing which group means are significantly different from the other group means , while also able to control the experiment wise error rate . It is also important to note that if a test is not significant,and there is no evidence in the data to reject the null, this means that there is also no evidence that suggests the group means to be different ,so there will be no need to perform post hoc tests.
6. Respond to the post below.
Analysis of Variance is a more versatile version of the t-test. Where t-test can only test two samples at a time, we can compare more than two samples using the ANOVA. We can also analysis more than one factor at a time in ANOVA, looking at potential significance in combinations of factors.
7. Respond to the post below.
The post hoc test is a test that is done after the ANOVA test so that the researchers may determine or identify exactly how different groups or populations differ from one another which is important for accuracy of the research being done. The ANOVA test does not provide as much flexibility in the differences of grouos. An experiment in which a researcher would run a post hoc test is how effective certain texting methods are on a group of students with different agr groups. This will help to see where there differences really come from.
8. Respond to the post below.
Post hoc tests are follow-up tests that are completed after an ANOVA test that showed a significant F-ratio. Because the ANOVA test does not show any specifics in which treatment condition was significantly different, there needs to be post hoc tests used after a null hypothesis is rejected. This test will help to determine which mean differences are significant or not. The post hoc test can only compare 2 means at a time, so there will need to be multiple tests completed. an experiment in which a researcher would use a post hoc test is if they were to test 3 different types of medication and their impact on anxiety. If the test concluded with a rejected the null and showed that there was a significant F-ratio, the researcher would be interested in finding out which medication showed a significant difference in anxiety symptoms for patients
