The research hypothesis is a vitally important element of any quantitative research study. This Discussion provides an opportunity for you to develop your own testable hypothesis based on the data set for the Final Project.
To prepare for this Discussion, review this week’s Learning Resources. Then, look at the data set for the Final Project (provided in this week's Final Project, Part I) and engage in the following steps:
• Develop a research question based on the variables in the data set.
• Formulate a null hypothesis and an alternative hypothesis.
• Consider the difference between a Type I error and a Type II error and think about how each of these might be demonstrated in this situation.
• Research what is meant by a "familywise error" and consider whether or not this type of error might be of concern in this situation.
Based on the data set for the Final Project, describe your research question, discuss your null and alternative hypotheses, provide an explanation of Type I and Type II errors, and explain why you think a familywise error would—or would not—be of concern. Be sure to provide a rationale for your response.
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Research question: Does educational level (variable) have a significant impact on income (variable) among adults in the dataset?
Null hypothesis (H0): There is no significant difference in income based on educational level among adults in the dataset.
Alternative hypothesis (HA): There is a significant difference in income based on educational level among adults in the dataset.
Explanation of Type I and Type II errors:
A Type I error occurs when the null hypothesis is rejected, despite it being true. In this context, it would mean concluding that there is a significant difference in income based on educational level when there isn't one. It is often referred to as a false positive.
A Type II error occurs when the null hypothesis is failed to be rejected, despite it being false. In this context, it would mean failing to conclude that there is a significant difference in income based on educational level when there actually is one. It is often referred to as a false negative.
In this situation, a Type I error would occur if we falsely conclude that educational level has a significant impact on income when it actually doesn't. This could happen due to random chance or sampling error.
A Type II error would occur if we fail to conclude that educational level has a significant impact on income when it actually does. This could happen if the sample size is too small or the effect size is too small to detect.
Familywise error refers to the potential for making multiple comparisons or conducting multiple hypothesis tests, leading to an increased likelihood of at least one Type I error. In this situation, it involves conducting multiple tests to compare income across different levels of educational attainment. If no measures are taken to control for familywise error, it could increase the chances of making a Type I error.
Whether or not familywise error is a concern depends on the specific analysis plan and the desired level of confidence in the results. If multiple comparisons are conducted without adjusting for familywise error, there is a higher risk of encountering false positives. However, if appropriate correction methods or adjustments (e.g., Bonferroni correction) are implemented, the risk can be minimized. The level of concern for familywise error ultimately depends on the researcher's tolerance for false positives and the importance of controlling for multiple comparisons in the specific study context.