Name: student's t-test for significance checks
Uploaded: 2024-04-04T10:51:12.000Z
Duration: 1 min 9 s
Channel: JoVE

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.C

comparing-experimental-results-student-s-t-test

Comparing Experimental Results: Student's t-Test

L’influence du changement de méthode, d’échantillon ou d’analyste sur les résultats de l’analyse est étudiée en ne modifiant qu’une seule fois sur une paire d’expériences.
Le test t compare la signification statistique d'une moyenne avec la valeur moyenne d'une autre ou avec une valeur connue.
Dans un test t, les données non appariées sont deux ensembles de données indépendants provenant de la même source. Les données appariées sont deux ensembles de données liées, par exemple, deux ensembles de deux méthodes différentes sur une série d’échantillons.
La statistique de test est dérivée de l’intervalle de confiance sélectionné pour une valeur connue. Par rapport à la valeur statistique de test tabalée pour un degré de liberté donné à un niveau de signification choisi, si la première valeur se situe dans la plage de la seconde, l'hypothèse nulle est acceptée.
Un test unilatéral recherche UNIQUEMENT une augmentation ou UNIQUEMENT une diminution, tandis qu’un test unilatéral recherche un changement dans l’une ou l’autre direction.

comparaison des résultats expérimentaux : test t de student

Le test t est une méthode statistique utilisée pour comparer la moyenne d'un échantillon avec une moyenne de population ou pour comparer deux moyennes de deux ensembles de données. La statistique de test est calculée à partir de l'écart type, de la moyenne et du nombre de mesures dans l'ensemble de données à un intervalle de confiance sélectionné, puis comparée à un tableau de valeurs critiques à ce niveau de confiance. Si la statistique du test est inférieure à la valeur critique, l'hypothèse nulle est acceptée. Dans ce cas, nous précisons que la différence entre les moyennes n’est pas statistiquement significative et provient donc d’erreurs indéterminées (aléatoires). Si la statistique du test est supérieure à la valeur critique, l'hypothèse nulle est rejetée. Dans ce cas, nous affirmons que la différence entre les moyennes est statistiquement significative et ne peut s’expliquer par des erreurs aléatoires. La différence vient d’erreurs de méthode, d’échantillonnage, des analystes eux-mêmes ou de véritables différences phénoménologiques. En statistiques, les tests t peuvent être effectués sur des données non appariées ou appariées. Les données non appariées sont deux ensembles de mesures répétées provenant de la même source ; les données appariées font référence à des données prélevées sur les mêmes échantillons ou sujets à partir de deux méthodes différentes ou à deux moments différents à des fins de comparaison. Lorsqu’un seul côté d’une courbe de distribution normale est utilisé dans le test t, il s’agit d’un test unilatéral. Si les deux côtés de la distribution sont utilisés dans le test t, le test est bilatéral.

Comparaison des résultats expérimentaux : test T de Student

L’influence du changement de méthode, d’échantillon ou d’analyste sur les résultats de l’analyse est étudiée en ne modifiant qu’une seule fois sur une paire d’expériences.
Le...

Education

Enseignement

JoVE Core

Analytical Chemistry

Chimie analytique

Unit 1

Unité 1

Chemical Applications of Statistical Analyses

Applications chimiques des analyses statistiques

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

TERMES ET CONCEPTS CLÉS

student's t-test for significance checks

Student's t-test is a statistical tool for checking whether two means are different in a meaningful way. It can compare a sample mean with a population mean, or it can compare the means from two data sets. The test uses the mean, standard deviation, and number of measurements, along with a chosen confidence interval.

The test statistic is then compared with a table of critical values at that confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. That means the difference between the means is not statistically significant and is likely due to random, indeterminate errors.

If the test statistic is higher than the critical value, the null hypothesis is rejected. In that case, the difference is statistically significant and cannot be explained by random errors alone. The cause may come from the method, the sampling, the analyst, or a real difference in the phenomenon being studied.

Student's t-tests can be used with unpaired or paired data. Unpaired data are two sets of replicate measurements from the same source. Paired data are measurements taken on the same samples or subjects with two methods, or at two different time points, for comparison. The test can also be one-tailed, when only one side of the normal distribution curve is used, or two-tailed, when both sides are used.

Student's t-Test for Significance Checks

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Learn how Student's t-test compares means, uses critical values, and helps decide whether a result is statistically significant in high school science.

Student's t-Test for Significance Checks | JoVE K12

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Student's t-Test for Significance Checks

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