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 Size Calculation01:19

Sample Size Calculation

3.3K
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...
3.3K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.3K
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...
5.3K
Margin of Error01:27

Margin of Error

4.1K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
4.1K
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 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

You might also read

Related Articles

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

Sort by
Same journal

Translational validity of rodent diet-induced obesity models for cognitive impairment in human metabolic disease.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Lateral dominance and (as)symmetry in 50 m all-out front crawl.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Vitamin A and its relationship with insulin secretion and diabetes mellitus: a review.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Abundance, biomass and variability of the demersal mesozooplankton during contrasting seasons on a shallow reef ecosystem.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Judicial decision sets Restinga coastal ecosystem at high risk in Brazil.

Anais da Academia Brasileira de Ciencias·2026
Same journal

Conservation, diversity, and evaluation of Cassava landraces for northern Mato Grosso.

Anais da Academia Brasileira de Ciencias·2026

Related Experiment Video

Updated: Jun 25, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K

Minimum sample size for estimating the Net Promoter Score under a Bayesian approach.

Eliardo G Costa1, Rachel Tarini Q Ponte2

  • 1Universidade Federal do Rio Grande do Norte, Departamento de Estatística, Av. Senador Salgado Filho, 3000, 59078-970 Natal, RN, Brazil.

Anais Da Academia Brasileira De Ciencias
|May 29, 2024
PubMed
Summary

This study introduces a Bayesian approach for estimating customer loyalty using the Net Promoter Score (NPS). It provides statistical methods and tools for sample size determination, enhancing NPS analysis in business contexts.

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K
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

14.5K

Related Experiment Videos

Last Updated: Jun 25, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.7K
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

14.5K

Area of Science:

  • Business Analytics
  • Statistical Modeling
  • Customer Relationship Management

Background:

  • The Net Promoter Score (NPS) is a widely adopted metric for gauging customer loyalty.
  • Despite its prevalence, rigorous statistical studies on NPS properties and sample size determination are limited.
  • Existing research often lacks robust statistical frameworks for NPS analysis.

Purpose of the Study:

  • To develop a Bayesian statistical framework for Net Promoter Score (NPS) estimation.
  • To address the scarcity of research on the statistical properties of NPS.
  • To provide practical methods for sample size determination in NPS surveys.

Main Methods:

  • A Bayesian approach was employed for point and interval estimation of NPS.
  • Statistical methodologies were developed to address sample size determination for NPS.
  • Computational tools were implemented to facilitate practical application of the proposed methods.

Main Results:

  • The study provides statistically sound point and interval estimators for NPS.
  • A clear methodology for determining appropriate sample sizes for NPS studies is presented.
  • The effectiveness of the Bayesian approach was demonstrated through an example in financial services.

Conclusions:

  • The proposed Bayesian methodology offers a robust framework for NPS analysis.
  • The developed tools and methods enhance the reliability of customer loyalty measurements.
  • This research contributes to a more statistically rigorous understanding and application of NPS in business settings.