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

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.7K
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...
7.7K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.3K
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 +...
8.3K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Surveys02:16

Surveys

14.8K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
14.8K
What are Populations and Communities?00:30

What are Populations and Communities?

33.9K
Overview
33.9K

You might also read

Related Articles

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

Sort by
Same author

Addressing discretization-induced bias in demographic prediction.

PNAS nexus·2025
Same author

Word embeddings quantify 100 years of gender and ethnic stereotypes.

Proceedings of the National Academy of Sciences of the United States of America·2018
See all related articles

Related Experiment Video

Updated: Jul 6, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

6.8K

Quantifying spatial under-reporting disparities in resident crowdsourcing.

Zhi Liu1, Uma Bhandaram2, Nikhil Garg3

  • 1School of Operations Research and Information Engineering, Cornell Tech, New York, NY, USA.

Nature Computational Science
|January 4, 2024
PubMed
Summary
This summary is machine-generated.

City residents report problems unevenly, causing service delays. This study introduces a new method to measure these reporting delays, revealing disparities and suggesting solutions for equitable city services.

More Related Videos

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.1K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

Related Experiment Videos

Last Updated: Jul 6, 2025

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

6.8K
Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.1K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

Area of Science:

  • Urban Studies
  • Data Science
  • Public Administration

Background:

  • Crowdsourcing is vital for modern city governance, enabling problem identification like downed trees and power lines.
  • Unequal reporting rates among residents lead to significant delays in addressing incidents, creating downstream service disparities.

Purpose of the Study:

  • To develop a novel method for identifying and quantifying resident reporting delays without external ground-truth data.
  • To analyze spatial and socioeconomic disparities in incident reporting timeliness.

Main Methods:

  • Leveraging the rate of duplicate reports for the same incident to infer reporting delays.
  • Applying the developed method to large-scale datasets of resident reports from New York City and Chicago.

Main Results:

  • Substantial spatial and socioeconomic disparities in incident reporting delays were identified in both cities.
  • The method was validated using external data, confirming its reliability in estimating reporting delays.

Conclusions:

  • Estimating reporting delays provides practical insights for improving government service efficiency and equity.
  • The findings highlight the need for targeted interventions to address reporting disparities and ensure equitable service delivery.