Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
Estimating Population Mean with Unknown Standard Deviation01:22

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...
Estimating Population Mean with Known Standard Deviation01:16

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...
Estimating Population Standard Deviation01:26

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...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mitigating <i>Legionella</i> spp. risk in an Australian healthcare facility using on-site electrochemical water disinfection.

Journal of water and health·2025
Same author

A diffusive gradients in thin-films (DGT) methodology for in situ measurement of imazamox and imazapyr herbicide residues.

Talanta·2025
Same author

Associations between life-course FEV<sub>1</sub>/FVC trajectories and respiratory symptoms up to middle age: analysis of data from two prospective cohort studies.

The Lancet. Respiratory medicine·2024
Same author

Global, regional, and national mortality burden attributable to air pollution from landscape fires: a health impact assessment study.

Lancet (London, England)·2024
Same author

Correction: Credentialed pharmacist-led home medicines reviews targeting treatable traits and their impact on health outcomes in people with chronic obstructive pulmonary disease: a pre- and post-intervention study.

International journal of clinical pharmacy·2024
Same author

Association between wildfire-related PM<sub>2.5</sub> and epigenetic aging: A twin and family study in Australia.

Journal of hazardous materials·2024

Related Experiment Video

Updated: May 20, 2026

Measuring the Switch Cost of Smartphone Use While Walking
07:00

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

A forecasting method to reduce estimation bias in self-reported cell phone data.

Mary Redmayne1, Euan Smith, Michael J Abramson

  • 1Graduate Diploma of Environmental Studies, School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington, New Zealand. mary.redmayne@gmail.com

Journal of Exposure Science & Environmental Epidemiology
|July 19, 2012
PubMed
Summary
This summary is machine-generated.

Accurate cell phone use recall is vital for health studies. A new Bayesian method improves forecasting, reducing bias and improving risk assessment for heavy users exposed to electromagnetic radiation.

More Related Videos

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

Related Experiment Videos

Last Updated: May 20, 2026

Measuring the Switch Cost of Smartphone Use While Walking
07:00

Measuring the Switch Cost of Smartphone Use While Walking

Published on: April 30, 2020

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

Area of Science:

  • Environmental Health
  • Epidemiology
  • Biostatistics

Background:

  • Concerns exist regarding potential negative health effects from prolonged cell phone electromagnetic radiation exposure.
  • Epidemiological studies assessing these risks often rely on self-reported cell phone usage, which is subject to significant recall bias.
  • Accurate measurement of cell phone use is crucial for reliable risk assessment in epidemiological research.

Purpose of the Study:

  • To develop and validate a novel forecasting method to mitigate estimation bias in recalled cell phone usage data.
  • To improve the accuracy of relative risk calculations for individuals with higher levels of cell phone use.
  • To provide a tool for epidemiological studies to obtain more precise estimates of cell phone use.

Main Methods:

  • A Bayesian approach was employed, utilizing data from a cross-sectional cluster survey of New Zealand adolescents.
  • Participants' recalled Short Message Service (SMS) texting usage was compared against actual usage data obtained from their service provider.
  • Actual cell phone use was established as the gold standard for evaluating estimation bias.

Main Results:

  • Significant estimation bias was identified, primarily due to large random errors in recalled cell phone use, consistent with existing validation studies.
  • Traditional regression models using recalled data were shown to exaggerate upper-end usage forecasts, leading to underestimation of heavy users' relative risk.
  • The developed Bayesian method demonstrated a substantial reduction in estimation bias compared to conventional methods.

Conclusions:

  • The proposed Bayesian forecasting method effectively reduces estimation bias in recalled cell phone usage data.
  • This method offers a more accurate approach to calculating relative risk, particularly for mid-to-heavy users, enhancing the reliability of epidemiological findings.
  • Application of this method in future studies, provided data compatibility, can lead to more precise health risk assessments related to cell phone use.