Lecture

Scientific Approach and Metrology Introduction

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In course

CH-108(a): Chemistry Laboratory Work I

Familiariser l'étudiant avec le travail au laboratoire. Travailler de façon quantitative et/ou qualitative. TP réalisés en relation avec les cours de chimie de 1ere année et complémentaires avec le contenu des enseignements TP2 dispensés au semestre suiva ...

Description

This lecture introduces the scientific approach, emphasizing the importance of experimentation and critical thinking in testing hypotheses. It explores the history of radioactivity discovery, focusing on Henri Becquerel's experiments. The lecture also covers key concepts in the scientific process, such as hypothesis testing and peer review. Additionally, it delves into metrology, explaining the measurement process, error analysis, and calibration techniques. Various types of errors in measurements, including systematic and random errors, are discussed, along with methods to minimize them. The importance of accuracy, precision, and fidelity in measurements is highlighted, along with the impact of calibration errors. Overall, the lecture provides a comprehensive overview of the scientific method and the fundamental principles of metrology.

Instructors (2)

Anne-Sophie Chauvin

Web of Science Researcher ID F-4905-2011 Google Scholar page: https://scholar.google.ch/citations?user=O_HhJUEAAAAJ&hl=fr&oi=ao ORCID number is 0000-0001-9222-3866 Anne-Sophie Chauvin studied chemistry and biology at the university Paris V-René Descartes in France where she did a PhD in organic chemistry, working on mimetic complexes of the active site of Nitrile Hydratase, under the supervision of Prof. Jean-Claude Chottard. On 1999 she moved for 20 months to the University of Geneva, for a post-doctoral stay under the supervision of Prof. Alexandre Alexakis, where she focused on the determination of the absolute configuration of chiral alcohols using Organophosphorous Diamine Derivatizing Agents by 31P and 1H NMR Spectroscopy. On 2000 she joined the group of Prof Jean-Claude G. Bünzli and was appointed part-time lecturer in 2001, assuming teaching and research responsibilities. On 2006, she obtained the habilitation to direct research from the University René Descartes (HDR, Paris V, France) and since october 2007 she is Maître d'Enseignement et de Recherche at the EPFL. In 2010, she joigned the Laboratory for Photonics and Interfaces (LPI), headed by Pr. Michaël Graëtzel. Since the end of 2014, with the arrival of Dr Marinella Mazzanti at EPFL, she is back to lanthanide chemistry, dealing with coordination polymers. Her research interests concern supramolecular chemistry with the design of ligands which form water soluble complexes with luminescent lanthanides in view of biological applications. She is also interested in the synthesis of ligands and polymers for the extraction of lanthanide ions with high selectivity, and in the development of invisibke inks. She also developed organic dyes for dyes sensitized solar cells DSSC. She is now focusing on coordination polymers with luminescent properties and catalytic activity. Anne-Sophie Chauvin is involved in the teaching of General and Analytical Chemistry for students enrolled on first year in Pharmacy and Biology (UNIL): ex-cathedra courses (Chimie Générale et analytique I et II, approfondissement en chimie analytique pour pharmaciens) and exercices. She is in charge of practical sessions for students enroled in chemistry, forensic sciences, pharmacy and biology. She is elected at the FSB Faculty Council and was member of the EPFL Assembly (AE) for 6 years, until 2018. She was member of the Management committee of the Cost CM 1006 action entitled Eufen: European F-Element Network. She is Member of the Swiss Chemical Society (SCS) and Fellow of the Royal Society of Chemistry (FRSC).

Julien Alexandre Andres

Official source

Related concepts (32)

Observational error

Observational error (or measurement error) is the difference between a measured value of a quantity and its true value. In statistics, an error is not necessarily a "mistake". Variability is an inherent part of the results of measurements and of the measurement process. Measurement errors can be divided into two components: random and systematic. Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measurements of a constant attribute or quantity are taken.

Standard error

The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation. If the statistic is the sample mean, it is called the standard error of the mean (SEM). The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance.

Scientific Revolution

The Scientific Revolution was a series of events that marked the emergence of modern science during the early modern period, when developments in mathematics, physics, astronomy, biology (including human anatomy) and chemistry transformed the views of society about nature. The Scientific Revolution took place in Europe in the second half of the Renaissance period, with the 1543 Nicolaus Copernicus publication De revolutionibus orbium coelestium (On the Revolutions of the Heavenly Spheres) often cited as its beginning.

Accuracy and precision

Accuracy and precision are two measures of observational error. Accuracy is how close a given set of measurements (observations or readings) are to their true value, while precision is how close the measurements are to each other. In other words, precision is a description of random errors, a measure of statistical variability. Accuracy has two definitions: More commonly, it is a description of only systematic errors, a measure of statistical bias of a given measure of central tendency; low accuracy causes a difference between a result and a true value; ISO calls this trueness.

Statistical hypothesis testing

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .

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