Physics Maths Engineering
Introducing prediction intervals for sample means
Peer Reviewed
Abstract
Researchers and practitioners are typically familiar with descriptive statistics and statistical inference. However, outside of regression techniques, little attention may be given to questions around prediction. In the current paper, we introduce prediction intervals using fundamental concepts that are learned in descriptive and inferential statistical training (i.e., sampling error, standard deviation). We walk through an example using simple hand calculations and reference a simple R package that can be used to calculate prediction intervals.
Key Questions
1. What is the primary objective of the study?
The study aims to introduce prediction intervals for sample means, providing a range within which future sample means are expected to fall, based on current sample data.
2. What methodology was employed in the research?
The authors explain the concept of prediction intervals using fundamental statistical concepts such as sampling error and standard deviation. They provide a step-by-step example, including hand calculations and references to an R package, to demonstrate how to calculate and interpret prediction intervals.
3. What were the main findings of the study?
The study highlights the utility of prediction intervals in statistical analysis, offering a probabilistic range for future sample means. This approach enhances the understanding of variability and uncertainty in sample data, which is crucial for researchers and practitioners in various fields.
Summary
Contini, Spence, and Stanley (2024) introduce prediction intervals for sample means, emphasizing their importance in statistical analysis. By providing a practical example and accessible explanations, the study enhances the understanding of variability and uncertainty in sample data, offering valuable insights for researchers and practitioners.
