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Random Error01:04

Random Error

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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Random and Systematic Errors01:20

Random and Systematic Errors

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Related Experiment Video

Updated: Nov 16, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

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Unstructured numerical intensity scales: Models, protocols and errors.

Sukanya Wichchukit1, Sean LaFond2, Michael O'Mahony3

  • 1Department of Food Engineering, Faculty of Engineering at Kamphaengsaen, Kasetsart University, Kamphaeng Saen, 73140, Thailand.

Food Research International (Ottawa, Ont.)
|March 1, 2021
PubMed
Summary
This summary is machine-generated.

Rank-rating protocols significantly outperform serial monadic protocols for rating tasks. Unstructured 30-point and line scales offer optimal performance when memory and category space are sufficient.

Keywords:
Absolute modelCategory scalesLine scalesRank-ratingRelative modelSerial monadic

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Area of Science:

  • Psychology
  • Human Perception
  • Quantitative Measurement

Background:

  • Assessing human ability to use different rating scales.
  • Investigating the impact of protocol and scale type on performance.
  • Controlling for perceptual variance in visual stimuli.

Purpose of the Study:

  • To compare the effectiveness of unstructured numerical category scales (9-point and 30-point) and a line scale.
  • To evaluate two distinct rating protocols: rank-rating and serial monadic.
  • To determine the influence of 'sufficient space' and memory effects on rating accuracy.

Main Methods:

  • 62 untrained subjects rated the heights of 12 visual stimuli (mung bean columns).
  • Performance measured by 'scaling errors' (rating taller columns lower) and 'dissimilarity scores' (mismatch with true values).
  • Experimental conditions included variations in scale type and protocol.

Main Results:

  • Rank-rating protocol yielded significantly fewer errors and lower dissimilarity scores than serial monadic protocol.
  • Serial monadic protocol performance was hampered by memory recall issues.
  • 9-point scales showed poorer performance due to insufficient categories ('sufficient space' limitation).

Conclusions:

  • Rank-rating protocols are superior for rating tasks, especially when stimuli cannot be viewed simultaneously.
  • 30-point and line scales, under rank-rating protocol with sufficient space and no memory load, provide optimal performance.
  • Scale design and protocol choice critically impact the accuracy of subjective ratings.