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Related Concept Videos

Contaminants and Errors01:16

Contaminants and Errors

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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
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The effect of statistical sampling error on reliability and validity.

B le Roux1

  • 1Applied Statics for Health and Community Studies, School of Computing and Management Sciences, Sheffield Hallam University.

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Summary
This summary is machine-generated.

Statistical sampling involves selecting a representative sample from a larger group to understand population characteristics. This process is crucial for assessing problems and evaluating interventions effectively.

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Area of Science:

  • Statistics
  • Data Analysis
  • Research Methodology

Background:

  • Understanding a statistical population is essential for problem-solving.
  • Assessing the severity, extent, and intervention effectiveness requires population data.

Purpose of the Study:

  • To define the fundamental purpose of statistical sampling.
  • To explain how sample data can represent a larger statistical population.

Main Methods:

  • The core method involves selecting a representative sample from a statistical population.
  • Data from the sample is analyzed to infer characteristics of the population.

Main Results:

  • A well-selected sample allows for meaningful descriptions of the statistical population.
  • This descriptive capability is key to addressing population-level issues.

Conclusions:

  • Statistical sampling is a vital tool for describing populations and informing action.
  • The validity of population descriptions relies on the quality of the sample and the inference process.