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Ideal Solutions

Ideal Solutions

According to Raoult’s law, the partial vapor pressure of a solvent in a solution is equal or identical to the vapor pressure of the pure solvent multiplied by its mole fraction in the solution. However, Raoult's Law is only valid for ideal solutions. For a solution to be ideal, the solvent-solute interaction must be just as strong as a solvent-solvent or solute-solute interaction. This suggests that both the solute and the solvent would use the same amount of energy to escape to the...

General Properties of Solutions

General Properties of Solutions

Many common substances around us exist as a solution, such as ocean water, air, and gasoline. All solutions are mixtures of substances that are composed of varying amounts of two or more types of atoms or molecules. A mixture with a non-uniform composition is a heterogeneous mixture, whereas a mixture with a uniform composition is a homogeneous mixture. The components that make the homogeneous mixture are evenly spread out and thoroughly mixed.

Solution Formation

Solution Formation

There is no one solvent that can dissolve every type of solute. Some substances that readily dissolve in a certain solvent might be insoluble in a different solvent. A simple way to predict which substances dissolve in which solvent is the phrase "like dissolves like". This means that polar substances, such as salt and sugar, dissolve in a polar substance like water. In contrast, non-polar substances are more soluble in non-polar solvents such as carbon tetrachloride.
This selective...

Enthalpy of Solution

Enthalpy of Solution

There are two criteria that favor, but do not guarantee, the spontaneous formation of a solution:

Standard Solutions

Standard Solutions

Standard solutions refer to solutions with a precisely known concentration or composition. A primary standard is a highly pure, high molar mass, stable substance that is entirely soluble in water, the most commonly used solvent in analytical chemistry. The primary standard solution can be used to standardize secondary standards, which are substances with known concentrations but are less pure and stable. Standard solutions are essential for achieving accurate and reliable results in analytical...

Blank Solutions

Blank Solutions

A blank solution is a solution that does not contain the analyte, or the substance of interest being tested or measured. It is typically prepared using the same reagents and procedure as the sample solution but without adding the analyte. The primary purpose of preparing a blank solution is to account for any background interference or contamination that may affect the accuracy and reliability of the analytical method.
In some experimental cases, the reagents, solvents, or lab equipment used in...

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Related Experiment Video

Updated: Feb 2, 2026

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

Published on: July 28, 2009

Should I test more babies? Solutions for transparent data peeking.

Esther Schott¹, Mijke Rhemtulla², Krista Byers-Heinlein¹

¹Concordia University, Canada.

Infant Behavior & Development

|November 25, 2018

Summary

Infant researchers can improve efficiency using data peeking with simple corrections. This method helps optimize participant use while minimizing false positives in developmental research.

Keywords:

Data peeking Infant research Optional stopping Sample size

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Area of Science:

Developmental Psychology
Infant Research Methodology

Background:

Infant research is time-intensive, creating pressure for efficient participant use.
Data peeking, or optional stopping, is a strategy used to optimize study efficiency.
However, data peeking can increase false positive rates, potentially compromising research integrity.

Purpose of the Study:

To explore methods for harnessing the benefits of data peeking in infant research.
To address the negative consequences of data peeking, such as increased false positives.
To propose simple, implementable corrections for efficient and reliable infant study designs.

Main Methods:

Review of existing data peeking strategies in developmental research.
Introduction of two transparently reportable corrections for data peeking.
Discussion of the applicability of these corrections within current infant research frameworks.

Main Results:

Data peeking can be effectively utilized in infant research with appropriate corrections.
Two corrections are presented: one for pre-study planning and one for ongoing data collection.
These corrections are easily integrated into existing research practices.

Conclusions:

Implementing simple corrections with data peeking enhances participant efficiency in infant studies.
Transparent reporting of these corrections improves the replicability of infant research findings.
This approach balances efficiency gains with the need for statistical rigor and reliable results.