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Related Concept Videos

Margin of Error01:27

Margin of Error

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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.
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Confidence Coefficient01:24

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Related Experiment Video

Updated: May 1, 2026

Screening for Endocrine Activity in Water Using Commercially-available In Vitro Transactivation Bioassays
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Confidence rating for eutrophication assessments.

Uwe H Brockmann1, Dilek H Topcu1

  • 1Hamburg University, Institute for Geology, Dept. Biogeochemistry and Marine Chemistry, Martin-Luther-King-Platz 6, 20146 Hamburg, Germany.

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|April 1, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to assess the reliability of monitoring data by combining statistical confidence limits with sampling representativeness. This approach ensures more balanced and accurate results for environmental measurements, like total nitrogen concentrations.

Keywords:
Confidence ratingEutrophication assessmentsGerman Bight/North SeaRepresentativenessTotal nitrogen

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Area of Science:

  • Environmental Science
  • Oceanography
  • Data Science

Background:

  • Data confidence relies on variability and sampling representativeness in space and time.
  • Variability is assessed using statistical confidence limits.
  • Sampling representativeness is linked to equidistant sampling and consideration of gradients.

Purpose of the Study:

  • To develop and propose a novel method combining data variability and sampling representativeness.
  • To achieve balanced and reliable results for environmental monitoring data.
  • To demonstrate the method's effectiveness using total nitrogen concentrations in the German Bight/North Sea.

Main Methods:

  • Dividing surface areas, vertical profiles, and time periods into regular sections for representativeness calculation.
  • Summing individual representativeness scores for overall assessment.
  • Estimating the impact of unsampled sections by adjusting confidence based on distance and interrupted gradients.
  • Rating time sections based on sampling rate differences and mean concentrations.

Main Results:

  • The proposed method successfully combines statistical confidence and sampling representativeness.
  • Balanced and reliable results were achieved for total nitrogen concentration data.
  • The method provides a comprehensive approach to evaluating monitoring data quality.

Conclusions:

  • The integrated method offers a robust framework for assessing the confidence of monitoring data.
  • This approach enhances the accuracy and reliability of environmental measurements.
  • The findings are crucial for effective environmental management and decision-making.