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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Uncertainty in Measurement: Accuracy and Precision03:37

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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. 
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Uncertainty: Overview00:59

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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.
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Clinically Relevant Drug Product Specifications: Methods of Establishment01:29

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Product specifications define the acceptable quality of a pharmaceutical product by ensuring identity, purity, potency, and strength. These specifications serve as benchmarks during development, manufacturing, and post-approval quality control. Clinically relevant specifications are particularly important because they directly relate to a drug's safety and efficacy in clinical use.Dissolution studies are critical biopharmaceutic tools that link in vitro behavior to in vivo performance. They...
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Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Related Experiment Video

Updated: Dec 26, 2025

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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The Goldilocks Challenge-Controlling Uncertainty When Setting Product Specifications.

Richard K Burdick1, Julia C O'Neill2

  • 1Burdick Statistical Consulting LLC, 7783 Renegade Hill Drive, Colorado Springs, CO 80923; and RickBASU@aol.com.

PDA Journal of Pharmaceutical Science and Technology
|March 18, 2020
PubMed
Summary

Setting product specifications too tightly early in manufacturing can lead to supply disruptions. This study shows how to control the probability of overly tight limits during early drug development.

Keywords:
Control strategyProcess capabilityProduct life cycle managementQuality by Design (QbD)SpecificationsStatistical process control (SPC)Tolerance interval

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Control
  • Regulatory Science

Background:

  • Product specifications are crucial for ensuring medical product safety and efficacy.
  • Current practices often rely on limited process experience for setting initial specifications.
  • Overly tight specifications can lead to supply chain issues and increased costs.

Purpose of the Study:

  • To demonstrate a method for controlling overly tight product specifications during early manufacturing.
  • To address the challenge of setting appropriate limits with limited process data.
  • To balance patient safety with manufacturing efficiency.

Main Methods:

  • Analyzing the impact of specification limits on process variability.
  • Developing strategies to manage uncertainty in early-stage manufacturing.
  • Controlling the probability of excursions beyond established limits.

Main Results:

  • Tightly set limits do not reduce inherent process variation.
  • Unrealistically tight limits increase the likelihood of product discards and supply disruptions.
  • A deliberate control strategy can mitigate risks associated with early-stage specifications.

Conclusions:

  • Early product specifications require careful consideration of natural process variability.
  • Controlling the probability of tight intervals is essential for efficient drug development.
  • Optimized specification setting ensures product quality while minimizing manufacturing risks.