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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
8.9K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.8K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.8K
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...
5.8K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.3K
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...
9.3K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

4.0K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
4.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.5K
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...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Respond to the commentary on "Government subsidy for infertility treatment: Impact on quality of life for infertile women undergoing in vitro fertilization-embryo transfer".

Journal of the Formosan Medical Association = Taiwan yi zhi·2026
Same author

Context matters: coordinated transcriptional regulation and root plasticity under multinutrient conditions.

The New phytologist·2026
Same author

Sex-specific alterations of niacin flush pathway biomarkers in schizophrenia.

Psychoneuroendocrinology·2026
Same author

Integration of Time-Varying Pharmacometric Modeling With Cox Regression for Time-to-Event Analysis in NONMEM.

CPT: pharmacometrics & systems pharmacology·2026
Same author

Male- and female-factor infertility and partners' quality of life during assisted reproductive technology: A dyadic actor-partner analysis.

Journal of the Chinese Medical Association : JCMA·2026
Same author

Development and evaluation of a predictive biomarker model based on the niacin flush pathway for differentiating schizophrenia and bipolar disorder.

Schizophrenia research·2026
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Apr 21, 2026

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis
08:36

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Published on: July 16, 2019

12.5K

Good-Turing frequency estimation in a finite population.

Wen-Han Hwang1, Chih-Wei Lin, Tsung-Jen Shen

  • 1Institute of Statistics and Department of Applied Mathematics, National Chung Hsing University, Taichung 40227, Taiwan.

Biometrical Journal. Biometrische Zeitschrift
|November 14, 2014
PubMed
Summary
This summary is machine-generated.

Good-Turing frequency estimation, a method for predicting class detection probabilities, is adapted for finite populations. This modification enhances its applicability in practical scenarios beyond infinite population assumptions.

Keywords:
Finite populationFrequency estimationGood-TuringNumber-of-classes estimationSample coverageShannon index

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K
Transgenic Rodent Assay for Quantifying Male Germ Cell Mutant Frequency
14:45

Transgenic Rodent Assay for Quantifying Male Germ Cell Mutant Frequency

Published on: August 6, 2014

16.9K

Related Experiment Videos

Last Updated: Apr 21, 2026

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis
08:36

Development of Targeting Induced Local Lesions IN Genomes TILLING Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Published on: July 16, 2019

12.5K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K
Transgenic Rodent Assay for Quantifying Male Germ Cell Mutant Frequency
14:45

Transgenic Rodent Assay for Quantifying Male Germ Cell Mutant Frequency

Published on: August 6, 2014

16.9K

Area of Science:

  • Statistics
  • Data Science
  • Computational Linguistics

Background:

  • Good-Turing frequency estimation is widely used for predicting detection probabilities of observed and unobserved classes.
  • Current methods often assume sampling with replacement or from infinite populations, limiting practical applications.

Purpose of the Study:

  • To modify the Good-Turing estimation method to accommodate finite population sampling.
  • To introduce practical extensions of the modified method.

Main Methods:

  • Developed a modified Good-Turing estimation algorithm for finite populations.
  • Proposed three practical extensions to the modified method.
  • Evaluated the performance of the modified method and its extensions through simulation experiments.

Main Results:

  • The modified Good-Turing method effectively accounts for finite population sampling.
  • The proposed extensions demonstrate practical utility and performance in simulations.
  • The study provides a more robust approach to frequency estimation in constrained sampling environments.

Conclusions:

  • The modified Good-Turing estimation method offers a valuable improvement for applications with finite populations.
  • The extensions enhance the method's versatility and accuracy in real-world data scenarios.
  • This work addresses a key limitation of traditional Good-Turing frequency estimation.