Error
Error resides on the statistical side of the fault line separating the deductive tools of mathematics from the inductive tools of statistics. On the mathematics side of the chasm lays perfect information, and on the statistics side exists estimation in the face of uncertainty. For the purposes of estimation, error describes the unknown, provides a basis for comparison, and serves as a hypothesized placeholder enabling estimation. This entry discusses the role of error from a modeling perspective and in the context of regression, ordinary least squares estimation, systematic error, random error, error distributions, experimentation, measurement error, rounding error, sampling error, and nonsampling error.
For practical purposes, the universe is stochastic. For example, any “true” model involving gravity would require, at least, a parameter for every particle ...
Looks like you do not have access to this content.
Reader's Guide
Descriptive Statistics
Distributions
Graphical Displays of Data
Hypothesis Testing
Important Publications
Inferential Statistics
Item Response Theory
Mathematical Concepts
Measurement Concepts
Organizations
Publishing
Qualitative Research
Reliability of Scores
Research Design Concepts
Research Designs
Research Ethics
Research Process
Research Validity Issues
Sampling
Scaling
Software Applications
Statistical Assumptions
Statistical Concepts
Statistical Procedures
Statistical Tests
Theories, Laws, and Principles
Types of Variables
Validity of Scores
- All
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
- Y
- Z