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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.2K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
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...
3.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.2K
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...
8.2K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
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 +...
8.9K
What are Estimates?01:06

What are Estimates?

5.3K
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...
5.3K
Sample Size Calculation01:19

Sample Size Calculation

3.6K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.6K

You might also read

Related Articles

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

Sort by
Same author

Why and How Women Masturbate, and the Relationship to Orgasmic Response.

Journal of sex & marital therapy·2020
Same author

Using the sample maximum to estimate the parameters of the underlying distribution.

PloS one·2019
See all related articles

Related Experiment Video

Updated: Aug 13, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.5K

Estimation of population parameters using sample extremes from nonconstant sample sizes.

Tiffany N Kolba1, Alexander Bruno1

  • 1Department of Mathematics and Statistics, Valparaiso University, Valparaiso, IN, United States of America.

Plos One
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces new unbiased estimators for population parameters using sample extremes, even with varying sample sizes. Results show reliable parameter estimation using average sample size, particularly with larger sample sizes.

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.7K

Related Experiment Videos

Last Updated: Aug 13, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.5K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.7K

Area of Science:

  • Statistics
  • Probability Theory

Background:

  • Estimating population parameters from sample extremes is crucial in statistical analysis.
  • Previous methods assumed constant sample sizes, limiting applicability in real-world scenarios.

Purpose of the Study:

  • To develop and evaluate unbiased estimators for normal and exponential distribution parameters using sample extremes.
  • To address the challenge of varying sample sizes in parameter estimation.
  • To assess the performance of existing estimators when using average sample size.

Main Methods:

  • Derivation of new unbiased estimators accounting for variable sample sizes.
  • Monte Carlo simulations to evaluate estimator accuracy and precision.
  • Analysis of the impact of sample size distribution characteristics on estimation error.

Main Results:

  • New unbiased estimators were derived for normal and exponential distributions with varying sample sizes.
  • Simulations indicate that using the average sample size yields reliable parameter estimates.
  • Estimation accuracy is maintained, especially with larger average sample sizes.

Conclusions:

  • The developed estimation framework provides robust parameter estimates from sample extremes, even with non-constant sample sizes.
  • The findings are applicable to biological data, as demonstrated by a plant pollination example.