Name: measuring error in scientific data
Uploaded: 2023-09-22T11:49:39.000Z
Duration: 1 min 20 s
Channel: JoVE

Les erreurs dans le processus de mesure sont un problème courant. De telles erreurs peuvent être classées en deux catégories : aléatoires et systématiques.
Les erreurs aléatoires sont le résultat d’un manque d’uniformité dans le processus de mesure ou de variations dans la quantité mesurée. Il en résulte des fluctuations, trop élevées ou trop basses, autour de la valeur réelle.
Prenons l’exemple d’un scientifique qui mesure la longueur d’un ver de terre à l’aide d’un pied à coulisse. L’incohérence du scientifique dans la lecture correcte des échelles, ou le mouvement continu du corps du ver de terre pendant la mesure, peut entraîner des mesures de longueur incorrectes.
L’erreur aléatoire ne peut pas être évitée, cependant, elle peut être moyennée par des essais répétés.
Les erreurs systématiques sont le résultat d’un problème persistant et entraînent une divergence constante dans les mesures. Ces erreurs ont tendance à être soit trop élevées, soit trop faibles par rapport à la valeur réelle. Par exemple, des poids mesurés à l’aide d’une balance mal calibrée.
Ceux-ci sont prévisibles et principalement liés aux instruments. Cependant, contrairement à l’erreur aléatoire, elle ne peut pas être moyennée par des mesures répétées.

erreurs aléatoires et systématiques

Les scientifiques font toujours de leur mieux pour enregistrer des mesures avec la plus grande précision. Cependant, parfois des erreurs se produisent. Ces erreurs peuvent être aléatoires ou systématiques. Les erreurs aléatoires sont observées en raison de l'incohérence ou de la fluctuation dans le processus de mesure, ou des variations dans la quantité elle-même qui est mesurée. Ces erreurs fluctuent en étant supérieures ou inférieures à la vraie valeur dans des mesures répétées. Considérons un scientifique mesurant la longueur d'un ver de terre à l'aide d'une règle. L'erreur aléatoire dans ce processus de mesure pourrait être le résultat de la méthode incohérente avec laquelle le scientifique lit les échelles, ou si le ver de terre se déplace. L'erreur aléatoire ne peut pas être évitée, cependant elle peut être compensée en effectuant des essais répétés.

Les erreurs systématiques résultent d'un problème persistant et entraînent un écart constant dans la mesure. Ces erreurs tendent à être constamment supérieures ou inférieures à la vraie valeur. Elles sont prévisibles et sont principalement d'origine instrumentale. Par exemple, une balance de pesée mal calibrée peut peser régulièrement des objets plus lourds que leur vraie valeur. Contrairement à l'erreur aléatoire, les erreurs systématiques ne peuvent pas être compensées en effectuant des mesures répétées.

Ce texte est adapté de <a href="https://openstax.org/books/chemistry-2e/pages/1-5-measurement-uncertainty-accuracy-and-precision">Openstax, Chemistry 2e, Section 1.5: Measurement Uncertainty, Accuracy, and Precision.</a>

Erreurs aléatoires et systématiques

Les scientifiques font toujours de leur mieux pour enregistrer les mesures avec la plus grande exactitude et précision. Cependant, des erreurs peuvent parfois se produire. Ces erreurs peuvent être ...

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Random and Systematic Errors

measuring error in scientific data

Random and systematic errors affect scientific measurements. Random errors cause values to spread around the true value. They make repeated measurements less consistent. Systematic errors shift measurements in the same direction each time.

Understanding both kinds of error helps students judge data quality. Random error can come from small changes during measurement. Systematic error can come from a biased tool or method. Scientists look for these patterns so they can improve results and interpret data more carefully.

Measuring Error in Scientific Data

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Random and systematic errors affect scientific measurements. Random errors cause values to spread around the true value. They make repeated measurements less consistent. Systematic errors shift measurements in the same direction each time. Understanding both kinds of error helps students judge data quality. Random error can come from small changes during measurement. Systematic error can come from a biased tool or method. Scientists look for these patterns so they can improve results and...

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Learn how random and systematic errors affect scientific measurements, why they matter, and how they change the way students judge data quality.

Measuring Error in Scientific Data | JoVE K12

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Measuring Error in Scientific Data

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