Reference no: EM131159849
1. Read transcript. What was interesting to you?
2. Why do you think it is important to understand correlation, especially within the criminal justice field? Do you think correlation is something that criminal justice professionals often rely upon in making decisions within their job responsibilities?
3. Can you think of examples of correlation that you might have used in your own life to help make an informed decision?
4. What is the importance of comparing mean differences? What statistical tests are available to compare differences between means?
5. Why is it important to compare means? How can information gathered by comparing means be used?
6. When would you use chi-square and why?
7. How are descriptive and inferential statistics used in the criminal justice research process?
8. What are some potential disadvantages or issues when researchers rely upon inferential statistics? Do you think that inferential statistics could lead to a wrong conclusion?
Transcript- Four Listen To Me First
Speakers: Narrator, SME
Narrator: Welcome to the Week Four podcast for CJA/335. This week, we'll discuss statistical significance and tests.
What does it mean to have statistical significance?
SME: First, statistical significance should not be confused with practical significance. Significance is traditionally interpreted as important meaning, whereas in statistics, significance really means that something is not by chance and is probably true. Statistical significance is measured mathematically. With statistical significance, a mathematically small value can be highly significant if the sample size is large enough. Finding statistical significance means an effect is "real" and not due to chance or coincidence. Mathematically, statistical significance can come down to an integer with two or more decimal places, such as 0.01, 0.001, and so on. Statistical significance is both about the probability and the strength of the relationship.
Narrator: When do we need to conduct significance tests on data?
SME: You usually conduct significance tests in instances when you want to determine if the evidence or data can refute the null hypothesis. The null hypothesis is that the correlation being hypothesized is not present. If the probability is less than 0.01, you have determined that the null hypothesis is likely false. Another way to say it is that you have a less than one percent probability that a dependency, effect, or correlation is by chance.
Narrator: And what key point should students focus on this week?
SME: Statistical significance and testing is in large part built on the knowledge and skills of correlations and data dependency. It has a lot to do with hypothesis testing and is integral to verifying and validating a research design and interpreting the data results. Students should explore the different significance tests and why one is used over another in different cases. Lastly, please don't forget, significance is an estimation and is not absolute.