Gross Error
Gross Error - Related Videos
01:00
Detection of Gross Error: The Q Test
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2024
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...
10:12
Gross Dissection of the Stomach of the Lobster, Homarus Americanus
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Cited by 5 •
2009
We describe the gross dissection of the stomach of the American lobster (Homarus americanus).
01:15
Systematic Error: Methodological and Sampling Errors
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2024
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors. Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
01:14
Fundamental Attribution Error
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2020
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...
01:17
Gross Anatomy of the Lungs
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2025
The lungs are a pair of vital organs connected to the trachea via the left and right bronchi. The base of these organs meets the dome-shaped muscle known as the diaphragm. Encased by the pleurae, the lungs contact the mediastinum. The right lung is shorter yet wider, and has a larger volume than the left lung. The left lung has an indentation known as the cardiac notch. The superior region of the lungs is referred to as the apex, whereas the base is the lower region near the diaphragm. The...
FAQs
Random variation produces the normal spread associated with a method, whereas a gross error creates an unusually large deviation from the true or expected value. The distinction matters because statistical treatment can describe or reduce random variation but cannot reliably eliminate a mistaken reading, incorrect record, wrong setting, or altered sample. Analysts must investigate the procedure itself.
A preventable mistake may occur during sampling, measurement, recording, reagent selection, instrument setup, or calculation. Each stage can change the material being analyzed or the numerical result, so the final concentration may be wrong even when later arithmetic is performed correctly. Locating the stage of failure helps analysts troubleshoot the procedure rather than merely adjust the data.
Repeating the analysis with careful technique provides a direct check on whether the unusual result arose from a preventable mistake. If the repeated result no longer shows the discrepancy, the analyst has evidence that the original measurement or handling should be examined. This approach addresses the source of the problem, unlike statistical processing applied only after data collection.
A result far from an expected value, an implausible concentration, or disagreement with a carefully repeated analysis can signal a problem requiring investigation. Analysts should review meniscus readings, written records, reagent identity, instrument settings, sample handling, and calculations. This review connects the numerical discrepancy to a specific procedural event and supports an informed decision about data validity.
The analyst should identify the likely mistake, correct the technique or procedure, and repeat the analysis when possible. The suspect result should not be retained simply because it can be included in a statistical calculation. Documenting the discrepancy and its correction supports data validation, helps troubleshoot the method, and reduces the chance that the same error will recur.
Recognizing these mistakes helps laboratories reject invalid results before reporting concentrations, evaluate whether a procedure was followed correctly, and improve reproducibility. It also strengthens confidence in accepted data because analysts have distinguished correctable procedural failures from normal measurement variation. In analytical chemistry, this review is important for validating results and maintaining reliable quality control.