Related Experiment Video
Updated: Jul 26, 2026

07:07
Errors as a Means of Reducing Impulsive Food Choice
Published on: June 5, 2016
Portion-size estimation training in second- and third-grade American Indian children.
J L Weber1, L Cunningham-Sabo, B Skipper
1Department of Physiology, College of Medicine, University of Arizona, Tucson 85721, USA. jweber@u.arizona.edu
The American Journal of Clinical Nutrition
|April 9, 1999
Summary
Portion-size estimation training improved dietary reporting accuracy in children. A single 45-minute session significantly reduced food quantity estimation errors for many foods, especially liquids.
Area of Science:
- Nutrition Science
- Pediatric Health
- Dietary Assessment
Background:
- Accurate dietary self-reporting is crucial for nutritional assessment.
- Portion-size estimation training enhances adult dietary reporting accuracy.
- Evidence for similar benefits in children is lacking.
Purpose of the Study:
- To evaluate the effectiveness of a portion-size estimation training session on children's food quantity estimation error.
- To determine if training effects vary by food type or measurement method.
Main Methods:
- A 45-minute hands-on training program was administered to American Indian schoolchildren.
- Control group (n=34) received no training.
- Estimation errors were measured using difference and absolute value methods pre- and post-training.
Main Results:
- Significant reduction in estimation error for 7 of 12 foods in the trained group.
- Greater error reduction observed in the trained group for 3 specific foods.
- Largest improvements noted for liquids (volume/label reading) and solid foods (cups).
Conclusions:
- A single portion-size estimation training session can improve children's food quantity estimation accuracy.
- Effectiveness varies by food type and measurement method.
- Multiple training sessions may be needed for substantial improvements in dietary reporting accuracy.
Related Concept Videos
What are Estimates?
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates.
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...
Estimating Population Mean with Known Standard Deviation
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Estimating Population Standard Deviation
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Estimating Population Mean with Unknown Standard Deviation
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
William S. Gosset (1876–1937) of the Guinness...

