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

Random Error01:04

Random Error

10.0K
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...
10.0K
Random and Systematic Errors01:20

Random and Systematic Errors

15.7K
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...
15.7K
Random and Systematic Errors01:20

Random and Systematic Errors

897
897
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.3K
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...
11.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.8K
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 +...
9.8K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

113.0K
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. 
113.0K

You might also read

Related Articles

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

Sort by
Same author

ST-GNNFormer: coupling dynamic graph learning and multi-scale temporal attention for traffic flow forecasting.

Scientific reports·2026
Same author

Tumor cell Jagged-1 promotes regional lymphatic metastasis and predicts recurrence in node-positive breast cancer.

Breast cancer research : BCR·2026
Same author

Hybrid Computer Vision Model to Predict Lung Cancer in Diverse Populations.

JCO clinical cancer informatics·2026
Same author

A phase 1 study of berzosertib (M6620, VX-970) in combination with cisplatin and radiation in patients with locally advanced head and neck squamous cell carcinoma (ETCTN 9950).

Cancer·2026
Same author

National utilization and outcomes of robotic pancreatoduodenectomy.

HPB : the official journal of the International Hepato Pancreato Biliary Association·2026
Same author

Glucose-to-albumin ratio predicts short-term mortality in critically ill patients with acute pancreatitis.

Scientific reports·2026
Same journal

Comparing Adaptive Interventions under a General Sequential Multiple Assignment Randomized Trial Design via Multiple Comparisons with the Best.

Journal of statistical planning and inference·2026
Same journal

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data.

Journal of statistical planning and inference·2026
Same journal

On semi-supervised estimation using exponential tilt mixture models.

Journal of statistical planning and inference·2025
Same journal

Regression-Assisted Bayesian Record Linkage for Causal Inference in Observational Studies with Covariates Spread Over Two Files.

Journal of statistical planning and inference·2024
Same journal

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Journal of statistical planning and inference·2024
Same journal

Distributed eQTL analysis with auxiliary information.

Journal of statistical planning and inference·2024
See all related articles

Related Experiment Video

Updated: Mar 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Rigorous Error Control Methods for Estimating Means of Bounded Random Variables.

Zhengjia Chen1, Xinjia Chen2

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322.

Journal of Statistical Planning and Inference
|September 20, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces novel sample size methods for estimating random variable means without distribution knowledge. These rigorous, non-approximated techniques reduce sample complexity and ensure statistical accuracy.

More Related Videos

Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

9.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Related Experiment Videos

Last Updated: Mar 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Errors as a Means of Reducing Impulsive Food Choice
07:07

Errors as a Means of Reducing Impulsive Food Choice

Published on: June 5, 2016

9.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Area of Science:

  • Statistics
  • Probability Theory
  • Mathematical Statistics

Background:

  • Estimating means of random variables often requires distributional assumptions.
  • Existing sample size methods may assume independent and identically distributed (i.i.d.) samples.
  • Approximations are common in sample size calculations, potentially affecting accuracy.

Purpose of the Study:

  • To develop rigorous sample size methods for estimating means of bounded random variables.
  • To provide methods that do not require knowledge of underlying distributions.
  • To enable sample size calculations without assuming sample independence or identical distribution.

Main Methods:

  • Proposed novel sample size calculation techniques.
  • Utilized a mixed error criterion to reduce sample complexity.
  • Derived explicit sample size formulae.

Main Results:

  • Developed sample size methods applicable to bounded random variables without distributional assumptions.
  • Demonstrated significant reduction in sample complexity using a mixed error criterion.
  • Provided exact sample size formulae ensuring statistical accuracy.

Conclusions:

  • The proposed methods offer a robust approach to sample size determination for mean estimation.
  • These techniques are valuable when distributional information is unavailable or samples are not i.i.d.
  • The derived formulae guarantee statistical accuracy without approximations.