Related Experiment Video
Updated: May 11, 2026
09:07
Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
Published on: September 16, 2015
Uncertainty of testing methods--what do we (want to) know?
Martin Paparella1, Mardas Daneshian, Romana Hornek-Gausterer
1Environmental Agency Austria, Vienna, Austria. martin.paparella@umweltbundesamt.at
ALTEX
|May 14, 2013
Summary
Understanding uncertainty in standard in vivo testing methods is key to advancing regulatory science. This knowledge supports the development and validation of novel in vitro and in silico approaches, promoting ethical animal testing alternatives.
Area of Science:
- Regulatory Science
- Toxicology
- Computational Biology
Background:
- Standard in vivo testing methods are crucial for regulatory decisions.
- Current understanding of data uncertainty in these methods can be improved.
- This uncertainty impacts the development and validation of alternative testing strategies.
Purpose of the Study:
- To provide an overview of uncertainty types in regulatory testing.
- To suggest methods for improving the knowledge base of uncertainty.
- To guide the use of uncertainty information for new testing approaches.
Main Methods:
- Literature review of uncertainty in standard testing methods.
- Analysis of quantitative and qualitative uncertainty types.
- Discussion of principles for using uncertainty information.
Main Results:
- Identified various types of uncertainties in standard in vivo data.
- Highlighted the importance of uncertainty accountability for new method validation.
- Proposed strategies for leveraging uncertainty knowledge.
Conclusions:
- Improved understanding of uncertainty in standard methods drives innovation in regulatory testing.
- Transparent uncertainty estimates support the 3Rs (Replacement, Reduction, Refinement).
- This work provides a foundation for developing and validating novel in vitro and in silico methods.
Related Concept Videos
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
03:37Uncertainty in Measurement: Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
01:15Data Validation
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
Key parameters for method validation include:
01:14Decision Making: Traditional Method
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...