In the United States, the goal of a criminal trial is to resolve accusations made against a person who is accused of committing a crime. In common law systems, most criminal defendants undergo a trial that is held before a jury and that is prosecuted by the prosecuting attorney/lawyer.

In criminal trials, there are four possible outcomes that comprise two correct outcomes and two incorrect outcomes. The two correct decisions are:

• The defendant did not commit a crime, and the jury determines the correct verdict of not guilty.
• The defendant did commit a crime, and the jury determines the correct verdict of guilty.

The two incorrect decisions are:

• The defendant is not guilty of a crime, but the jury determines an incorrect verdict of guilty. Statisticians refer to this error as a Type I error. Sometimes it is referred to as a false positive.
• The defendant is guilty of a crime, but the jury determines an incorrect verdict of not guilty. Statisticians refer to this error as a Type II error. Sometimes it is referred to as a false negative.

Consider the following table:

Table I: In a courtroom case, if a defendant is found not guilty, it does not necessarily mean innocence; rather, it means that there is not enough evidenceto support the verdict that the defendant is guilty.

		Reality
		Not Guilty	Guilty
Verdict	Not Guilty		Type II error ()
Guilty	Type I error ()

Table II:With the courtroom case above, the jury is trying to determine if the evidence presented corresponds to the guilt or innocence of the defendant. With inferential statistics, a researcher is trying to determine from the evidence whether or not a meaningful correlation exists between a dependent variable and an independent variable. To put it plainly, does a change in the independent variable correspond to a change in the dependent variable. If a relationship/difference is rejected, it does not mean that there is no relationship/difference; rather, it means that there is not enough evidence to support the decision that there is a relationship/difference.

		Reality
		Do Not Reject	Reject
Decision	Do Not Reject		Type II error ()
Reject	Type I error ()

Inferential statistics involves testing one or more hypotheses that stem from the research question(s) that attempt to establish whether a relationship/difference exists among the variable in the data. These hypotheses can test either for relationships or differences.

Analyze types of data analyses that can be conducted using the General Linear Model.

To Prepare:

• Review and complete the General Linear Model Worksheet Download General Linear Model Worksheet, identifying the most appropriate analysis for each worksheet item (i.e., correlation analysis, independent samples test, or descriptive statistics).
• Select two scenarios from the worksheet that can best be analyzed using correlation analysis.
• Select two scenarios from the worksheet that can best be analyzed using independent samples test.
• Identify a phenomenon in your field of study or discipline and consider how it could be studied using two types of analyses.

What are two examples of research scenarios that are best studied using correlation analysis and two examples of research scenarios that are best studied using independent samples t test. Explain how these two types of analyses could be applied to study a phenomenon in your field or discipline.

General Linear Model Worksheet

PSYC 6800: Applied Psychology Research Methods General Linear Model Worksheet Instructions: For each item below, indicate the most appropriate statistical analysis: correlational analysis, independent samples t test, or descriptive statistics 1. A researcher, Dr. I. M. Nosy, was interested in determining the relationship between the number of hours studied by undergraduate students and their mathematics achievement, as measured by a classroom examination (range of scores = 0 to 100). Most appropriate analysis: 2. Dr. I. M. Nosy wanted to determine whether there are gender differences in recycling behavior, as measured by the percentage of times that recyclable items are recycled. Most appropriate analysis: 3. Dr. I. M. Nosy wanted to determine whether performance on a science examination (range of scores = 0 to 100) among graduate students varied as a function of whether or not they received a good night's sleep the night before the examination (i.e., Yes vs. No). Most appropriate analysis: 4. Dr. I. M. Nosy wanted to determine the prevalence rate of divorce (range of scores = 0% to 100%) among married couples with no children. Most appropriate analysis: 5. Dr. I. M. Nosy wanted to determine the number of divorces who use online dating sites to develop relationships. Most appropriate analysis: 6. Dr. I. M. Nosy wanted to determine the link between age and job satisfaction among charity workers. Most appropriate analysis: 7. Dr. I. M. Nosy wanted to compare adults living in urban areas and adults living in rural areas with respect to alcohol consumption. Most appropriate analysis:

2019 Walden University 8. Dr. I. M. Nosy wanted to predict the prevalence rate of smoking by the age of the adult. Most appropriate analysis: 9. Dr. I. M. Nosy wanted to examine the difference between Millennial adults and Generation X adults with respect to attitudes toward using Facebook. Most appropriate analysis: 10. Dr. I. M. Nosy wanted to determine the proportion of sixth graders in the United States who have been bullied at least once. Most appropriate analysis: 11. Dr. I. M. Nosy was interested in determining the association between college professors' salaries and their attitudes toward gender differences in the salaries of college professors, as measured via a survey. Most appropriate analysis: 12. Dr. I. M. Nosy wanted to determine the graduation rate among graduate students at Walden University. Most appropriate analysis