I know there has been a delay of two years in the postings at Statistical Sage. For the past two years, I have been serving as an interim administrator. However, I am returning to faculty, straight back to a half a year sabbatical! I am happy to also be returning to posting here about teaching applied statistics.
As I was weighing the cost and benefits of leaving the classroom for two years, it never occurred to me that my time as an administrator would increase my resolve for the importance of the highest quality curriculum and teaching for applied statistics classes, but of all that occurred in the past two years, that is exactly what happened.
As you see, administrators make countless decisions each and every week. Some of those decisions are fairly minor in nature, while others have huge and lasting impacts on the university, college, faculty, students, and in the case of state sponsored universities … the tax payers. Often, though not nearly as often as I would like, data are at the center of those decisions, making it critical to have people in administration with at least some fundamental understanding of the use of statistics in decision making.
Now, before people start getting upset stating that we cannot quantify all that is important in a classroom and university setting, I openly admit that is true.
Though it is true that not everything can be quantified and formally assessed, much can be. I will review the most critical information that needs to be covered in an applied statistics class designed for training college administrators.
Over the next few weeks, I will be covering each of these topics in more detail as to how to best deliver this material.
- Epistemology, Decision Making, and Statistics
- Cognitive Biases; How Statistics can be used to get to the Truth
- Detecting Data Integrity Issues
- Data Management Protocol
- Populations vs. Samples
- Observational Errors: Measurement, Experimental, and Sampling
- Quality Decisions are Limited by the Quality of Measures
- Sampling and Quality Decisions
- Statistics and Sampling Error
- Parameters and Mathematical Modeling vs. Inferential Statistics (Introduction)
- Mathematical Modeling, Parameters, and Assumptions
- Statistical Decision Errors: Type I and Type II
Though these topics will be directly targeted toward how to teach a university administrator how to be a great data driven decision maker, this information is equally useful to anyone in any position to make data driven decisions, and foundational for any class in applied statistics, regardless of the audience.
In the end, quality decisions based on data are only as good as the integrity and the ability of the person making it. Though not every decision in academia should be a data driven decision, the quality of such decisions are limited by the quality of the data, which are limited by the quality of the measure and the quality of the sample. Such decisions are also extremely constrained by the appropriate use of the appropriate statistic. Over the next several weeks, I look forward to reviewing this information in more detail.