|
Activity Number:
|
424
- SPEED: Statistical Education
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 1, 2017 : 3:05 PM to 3:50 PM
|
|
Sponsor:
|
Section on Statistical Education
|
|
Abstract #325118
|
|
|
Title:
|
A prediction model for understanding statistical replication
|
|
Author(s):
|
Andrew Neath*
|
|
Companies:
|
SIU Edwardsville
|
|
Keywords:
|
hypothesis testing ;
effect size
|
|
Abstract:
|
There is growing concern over the number of scientific findings that fail when replication is attempted. Traditional statistical inference is designed as a look back to how data originates. Perhaps we also need to look ahead in anticipation of what data we will see next. Through the use of a Bayesian prediction model, this paper seeks to determine what can reasonably be expected to occur in a replication trial.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2017 program
|
