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Related Concept Videos

Self-Discrepancy Theory02:45

Self-Discrepancy Theory

One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.
Self-Discrepancy and Its Effects01:29

Self-Discrepancy and Its Effects

Self-discrepancy theory explains how people compare their actual self to their ideal and ought selves and how mismatches between these self-guides can lead to emotional distress. Developed by E. Tory Higgins, the theory distinguishes among three components of self-concept: the actual self, the ideal self, and the ought self. These refer respectively to how individuals perceive themselves, how they aspire to be, and how they believe they are obligated to be. Emotional well-being, self-esteem,...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Quality Control01:05

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...

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Related Experiment Video

Updated: May 15, 2026

Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition
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Troubleshooting and Quality Assurance in Hyperpolarized Xenon Magnetic Resonance Imaging: Tools for High-Quality Image Acquisition

Published on: January 5, 2024

The hidden quality gap in discovery.

David Hampton1, Toby Winchester, Pauline Carnell

  • 1SSA & Company Management Consultants, Berkeley Square House, Berkeley Square, London W1J 6BD, UK. dhampton@ssaandco.com

Drug Discovery Today
|January 23, 2013
PubMed
Summary

Flawed measurement equipment can hinder drug discovery progress. Systematic analysis and correction of measurement systems are crucial for reliable scientific results.

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Area of Science:

  • Pharmaceutical Sciences
  • Analytical Chemistry
  • Quality Control

Background:

  • Technological advancements in drug discovery are significant.
  • Measurement equipment issues can compromise research integrity.
  • Detecting measurement system flaws often requires dedicated investigation.

Purpose of the Study:

  • To describe measurement systems analysis fundamentals.
  • To highlight the importance of precision in measurement.
  • To present case studies of flawed measurement systems and their corrections.

Main Methods:

  • Fundamentals of measurement systems analysis.
  • Focus on the critical aspect of measurement precision.
  • Analysis of three distinct case studies involving measurement systems.

Main Results:

  • Identified flawed measurement systems through systematic analysis.
  • Demonstrated the impact of precision issues on research.
  • Provided examples of corrective actions for measurement system deficiencies.

Conclusions:

  • Measurement systems analysis is vital for ensuring data reliability.
  • Addressing precision issues is key to validating scientific findings.
  • Systematic evaluation and correction of equipment are necessary for robust drug discovery.