A:
Type I error is a false positive while type II error is a false negative. The former occurs if we reject a null hypothesis, which is correct (Zararsiz, 2014). It leads to acceptance of the alternative and affects reliability of the tests. The latter results from failure to disapprove the null hypothesis when it should be rejected. Researchers can avoid it by increasing the sample sizes.Statistical significance refers to the probability of an association between two or more parameters. Therefore, it aids in acceptance or rejection of the null hypothesis (Mohajeri & Mostafa, 2020). Practical significance illustrate a true association between parameters that exist in real life. the analysts uses size of required outcomes and association to make conclusion. They do not focus on the p-values and significance values applied in statistical significance.Researchers require several elements to undertake any hypothesis test. First, they have to formulate the null hypothesis and the alternative hypothesis (Lind, Marchal, & Wathen, 2017). The hypotheses will outline the rejection and acceptance region. Second, they rely on different factors to test the hypothesis. Some of the factors include sample sizes, significance value, and test statistics. The researchers ensures the sample size is large enough to lower margin of error.
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B:
When a researcher does not agree with a null hypothesis, and the null hypothesis is the real truth about the population, it is described as a type I error associated with the testing of the hypothesis (Dutta et al., 2020). This error is also known as a false positive since it is rejected but is true. The type II error is the exact opposite of type I. this is the situation in which a researcher accepts the null hypothesis, and this null hypothesis is very wrong in the population. The other name for a type II error is a false negative since it is an accepted yet very wrong hypothesis about the population.Statistical significance is achieved During hypothesis testing when the null hypothesis is right for the population. The null hypothesis, therefore, describes that a relationship exists between the variables being tested (Mourougan, & Sethuraman, 2017). This is to means that the evidence required while conducting the hypothesis has been found therefore the test is statistically significant.Moreover, practical significance means that it tests the size or strength of the relationship between variables (Mourougan, & Sethuraman, 2017). Besides, statistical significance does not analyze the strength of the effect. With practical, an investigator will get to know if the test is big enough to have a significant value, or small enough to be ignored.Hypothesis testing entails null hypothesis and alternate hypothesis. Null describes no association between variables while alternate describes a relationship between variables being tested (Mourougan, & Sethuraman, 2017). Other elements include the significance levels of the test, the calculation of test static value, and computing conclusions to the test.
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APA Format
Recommended Textbooks:
1.Discovering Statistics, by Hawkes and Marsh. Published by Hawkes Learning Systems. Chapters 11 and 12.
2.Lind, Marchal, Wathen, Statistical Techniques in Business and Economics, 16th Edition. Chapters 8.
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