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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Percentile01:18

Percentile

8.1K
A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
8.1K
Outliers and Influential Points01:08

Outliers and Influential Points

5.6K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.6K
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

3.1K
A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
3.1K
Interval Level of Measurement00:55

Interval Level of Measurement

17.6K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
17.6K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

5.2K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
5.2K

You might also read

Related Articles

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

Sort by
Same journal

Injustice flows along Itaya River: capabilities from living with river rhythmicity in Bajo Belén, Iquitos, Peru.

Sustainability science·2026
Same journal

Navigating lock-ins for adaptation: A case study of grid capacity planning in the Dutch energy transition.

Sustainability science·2026
Same journal

Differentiated characteristics, sustainability performance and preferences among small-scale aquaculture producers: implications for sustainable intensification.

Sustainability science·2026
Same journal

Unveiling the role of higher education institutions in regional sustainability transitions: a systematic literature review and research agenda.

Sustainability science·2026
Same journal

Field robots for weed control? Analyzing socio-technical change by looking at farming practices.

Sustainability science·2026
Same journal

A method to identify positive tipping points to accelerate low-carbon transitions and actions to trigger them.

Sustainability science·2026

Related Experiment Video

Updated: Dec 10, 2025

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.2K

Setting 'poverty thresholds': whose experience counts?

Stuart Colin Carr1

  • 1Massey University College of Humanities and Social Sciences, Auckland, New Zealand.

Sustainability Science
|September 3, 2020
PubMed
Summary
This summary is machine-generated.

Poverty thresholds should be empirically measured by changes in quality of life, not just income. This approach identifies genuine thresholds for escaping poverty traps and achieving Sustainable Development Goals (SDGs).

Keywords:
Eradicating povertyEvaluationMeasuring povertySDG1

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

863
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K

Related Experiment Videos

Last Updated: Dec 10, 2025

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.2K
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

863
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.7K

Area of Science:

  • Sustainability Science
  • Economics
  • Sociology

Background:

  • Poverty eradication is linked to achieving UN Sustainable Development Goals (SDGs), requiring access to healthcare, education, and livelihoods.
  • Current poverty threshold estimations rely on cost of living, creating a circular definition and untested link to quality of life.
  • Measuring income independently of quality of life is possible for the middle class, suggesting a similar approach for poverty.

Purpose of the Study:

  • To propose and validate an empirical method for identifying genuine poverty thresholds.
  • To link monetary thresholds directly to measurable changes in quality of life.

Main Methods:

  • Utilizing quality of work-life studies with multiple indicators.
  • Analyzing data to identify thresholds where quality of life shows marked upward shifts.
  • Extending the concept of work-life balance to include broader SDG indicators.

Main Results:

  • Identification of at least three distinct thresholds where quality of life significantly improved.
  • Discovery of inter-threshold ranges with zero to positive gradients in quality of life.
  • Empirical evidence supporting a direct relationship between monetary levels and quality of life.

Conclusions:

  • Poverty thresholds can be empirically defined by observable changes in quality of life.
  • This approach offers a more robust method for poverty assessment and SDG evaluation.
  • The framework can be extended to assess progress on broader sustainability and quality of life goals.