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Sampling Plans

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Decision Making: P-value Method

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...

Contaminants and Errors

Contaminants and Errors

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.
Another key consideration is determining the appropriate number of samples required to...

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Related Experiment Video

Updated: Mar 29, 2026

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

A Sampling Framework for Uncertainty in Individual Environmental Decisions.

Mirta Galesic^1,2, Astrid Kause¹, Wolfgang Gaissmaier^1,3

¹Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development.

Topics in Cognitive Science

|November 24, 2015

Summary

Environmental decisions are complex due to uncertainty. This study classifies uncertainty sources for individuals and links them to public policy strategies to aid environmental behavior choices.

Keywords:

Climate change Decision making Environment Public policy Risk Sampling Uncertainty

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Area of Science:

Environmental Science
Climate Science
Behavioral Economics

Background:

Environmental and climate decision-making is significantly impacted by uncertainty.
Individuals face various uncertainties when making environmental behavior choices.
Understanding these uncertainties is crucial for effective policy development.

Purpose of the Study:

To classify the diverse sources of uncertainty individuals encounter in environmental decision-making.
To connect identified uncertainty sources with targeted public policy interventions.
To enhance individual capacity to navigate uncertainty for improved environmental behavior.

Main Methods:

Utilized a statistical sampling framework to categorize uncertainty sources.
Analyzed individual decision-making processes in the environmental domain.
Mapped classified uncertainties to potential public policy strategies.

Main Results:

Developed a novel classification of individual-level uncertainties in environmental behavior.
Identified specific policy strategies corresponding to distinct uncertainty types.
Demonstrated a framework for policy design that addresses climate decision-making uncertainty.

Conclusions:

Addressing uncertainty is key to promoting effective environmental behavior.
Tailored public policies can significantly help individuals manage environmental decision-making uncertainty.
This research provides a foundation for evidence-based policies in climate and environmental action.