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'influenza del cambiamento del metodo, del campione o dell'analista sui risultati dell'analisi viene studiata alterando solo uno dei due esperimenti.
Il t-test confronta la significatività statistica di una media con il valore medio di un'altra o con un valore noto.
In un t-test, i dati non accoppiati sono due insiemi indipendenti di dati provenienti dalla stessa fonte. I dati accoppiati sono due insiemi di dati correlati, ad esempio due insiemi di due metodi diversi su una serie di campioni.
La statistica del test è derivata dall'intervallo di confidenza selezionato per un valore noto. Rispetto al valore statistico del test tabulato per un dato grado di libertà a un livello di significatività scelto, se il primo valore è all'interno dell'intervallo del secondo, l'ipotesi nulla è accettata.
Un test a una coda cerca SOLO un aumento o SOLO una diminuzione, mentre un test a due code cerca un cambiamento in entrambe le direzioni.

confronto dei risultati sperimentali: test t di student

Il t-test è un metodo statistico utilizzato per confrontare la media del campione con la media della popolazione o per confrontare due medie di due set di dati. La statistica del test viene calcolata dalla deviazione standard, dalla media e dal numero di misurazioni nel set di dati a un intervallo di confidenza selezionato e quindi confrontata con una tabella di valori critici a questo livello di confidenza. Se la statistica del test è inferiore al valore critico, viene accettata l’ipotesi nulla. In questo caso affermiamo che la differenza tra le medie non è statisticamente significativa e quindi deriva da errori indeterminati (casuali). Se la statistica del test è superiore al valore critico, l’ipotesi nulla viene rifiutata. In questo caso affermiamo che la differenza tra le medie è statisticamente significativa e non può essere spiegata da errori casuali. La differenza deriva da errori nel metodo, nel campionamento, negli stessi analisti o da vere e proprie differenze fenomenologiche. In statistica, i test t possono essere eseguiti sia su dati non accoppiati che su dati accoppiati. I dati non accoppiati sono due serie di misurazioni replicate dalla stessa fonte; i dati accoppiati si riferiscono ai dati prelevati sugli stessi campioni o soggetti con due metodi diversi o in due momenti temporali diversi per il confronto. Quando nel test t viene utilizzato solo un lato della curva di distribuzione normale, si tratta di un test a una coda. Se nel test t vengono utilizzati entrambi i lati della distribuzione, il test è a due code.

Confronto dei risultati sperimentali: test T di Student

L'influenza del cambiamento del metodo, del campione o dell'analista sui risultati dell'analisi viene studiata alterando solo uno dei due esperimenti.
Le...

Education

Didattica

JoVE Core

Analytical Chemistry

Chimica Analitica

Unit 1

Unità 1

Chemical Applications of Statistical Analyses

Applicazioni chimiche delle analisi statistiche

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

TERMINI E CONCETTI CHIAVE

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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key terms and concepts

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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