Name: significance testing in analytical chemistry
Uploaded: 2024-04-04T10:51:12.000Z
Duration: 1 min 4 s
Channel: JoVE

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High School

Chemistry

CHEM.2

CHEM.2.C

Significance Testing: Overview

La differenza tra i due valori è dovuta a un errore casuale inspiegabile o a un errore sistematico che può essere razionalizzato da un modello ipotetico?
Il test di significatività è un'analisi statistica utilizzata per convalidare se la differenza tra due valori può essere spiegata da errori indeterminati o meno.
L'ipotesi nulla presuppone che i valori confrontati siano gli stessi e che qualsiasi differenza derivi da errori indeterminati. L'ipotesi alternativa afferma che la differenza deve essere reale e non può essere spiegata da errori indeterminati.
Un livello di significatività indicato con α stabilisce una condizione di livello di confidenza per la validità dell'ipotesi nulla. L'ipotesi nulla viene rifiutata quando i valori sono presenti oltre il livello di confidenza.
Il test di significatività è chiamato a una coda se il rifiuto si verifica per valori a una sola estremità della normale curva di distribuzione. Nei test di significatività a due code, il rifiuto può verificarsi per valori che cadono alle due estremità della normale curva di distribuzione.

test di significatività: panoramica

Il test di significatività è un insieme di metodi statistici utilizzati per verificare se un'affermazione relativa a un parametro è valida. In chimica analitica, il test di significatività viene utilizzato principalmente per determinare se la differenza tra due valori deriva da errori determinati o casuali. L'effetto di una particolare modifica nel protocollo di misurazione, nell'analista o nel campione stesso può causare una deviazione dal risultato atteso. Nel caso di una sospetta deviazione/valore anomalo, dobbiamo essere in grado di confermare matematicamente che la deviazione proviene da una determinata fonte e che l'osservazione con la deviazione può essere logicamente omessa dall'analisi.

Due ipotesi vengono utilizzate come criteri per i test di significatività. L'ipotesi nulla (H0) afferma che i valori confrontati non differiscono significativamente tra loro. In altre parole, se esiste qualche differenza tra due valori, viene attribuita a un errore indeterminato. L'ipotesi alternativa (HA) afferma che i valori confrontati non sono uguali e la differenza è più significativa di quanto possa essere spiegata da un errore indeterminato.

Prima di eseguire il test, è necessario formulare le ipotesi e impostare un livello di significatività (α). La statistica del test, basata sulla media campionaria e sulla deviazione standard, viene quindi calcolata e confrontata con i valori tabulati, che sono fissati a particolari livelli di significatività e definiti come a una o due code. Se la statistica test calcolata supera i valori critici (statistica tabellata), si rifiuta l'ipotesi nulla e si afferma che la differenza tra i due valori non può essere spiegata da un errore casuale e indeterminato.

Nei test di significatività a una coda, l'ipotesi alternativa può specificare che il valore osservato è superiore o inferiore al valore atteso, ma non entrambi. Nei test di significatività a due code, l'ipotesi alternativa può semplicemente affermare che il valore osservato non è uguale al valore atteso, indipendentemente dalla direzione.

I test di significatività possono essere utilizzati su diversi parametri statistici di uno o più set di dati. Ai test vengono assegnati nomi diversi a seconda dei parametri o dello scopo. Il test di significatività viene spesso applicato per confrontare un valore osservato con la media o per confrontare due medie di due diversi set di dati. Questi test sono noti come test t. È possibile eseguire test di significatività anche sulla varianza di due insiemi di dati. In questo caso, il test è noto come test F. Se viene utilizzato un test di significatività per identificare i valori anomali, il test è chiamato test Q.

Test di significatività: panoramica

La differenza tra i due valori è dovuta a un errore casuale inspiegabile o a un errore sistematico che può essere razionalizzato da un modello ipotetico?
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

significance testing in analytical chemistry

Significance testing helps analytical chemists decide whether a difference is meaningful or just caused by random error. It is used to test whether a claim about a parameter is valid. In analytical chemistry, it mainly helps determine whether a change between two values comes from determinate error or indeterminate error.

A determinate error has a clear source. It may come from a change in the measurement protocol, the analyst, or the sample itself. These changes can create a deviation from the expected result. If a value looks like an outlier, significance testing can help show mathematically that the deviation comes from a determinate source and that the value can be logically left out of the analysis.

Significance testing begins with two hypotheses. The null hypothesis, H0, says the values being compared do not differ significantly. If any difference exists, it is treated as an indeterminate error. The alternate hypothesis, HA, says the values are not equal and that the difference is too large to be explained by indeterminate error.

Before the test is performed, the hypotheses must be stated and a significance level, α, must be set. The test statistic is then calculated from the sample mean and standard deviation. This value is compared with tabulated critical values, which are defined at specific significance levels and can be one-tailed or two-tailed. If the calculated test statistic is greater than the critical value, the null hypothesis is rejected.

A rejected null hypothesis means the difference cannot be explained by random, indeterminate error. In one-tailed significance testing, the alternative hypothesis says the observed value is either higher or lower than expected, but not both. In two-tailed testing, the alternative hypothesis only says the observed value is not equal to the expected value, without naming a direction.

Significance testing can be used on different statistical parameters from one or more data sets. When it compares an observed value with a mean or compares two means, it is called a t-test. When it compares the variance of two data sets, it is called an F-test. When it is used to identify outliers, it is called a Q-test.

Significance Testing in Analytical Chemistry

significance-testing-in-analytical-chemistry

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significance testing: overview

Significance testing helps analytical chemists decide whether a difference is meaningful or just caused by random error. It is used to test whether a claim about a parameter is valid. In analytical chemistry, it mainly helps determine whether a change between two values comes from determinate error or indeterminate error. A determinate error has a clear source. It may come from a change in the measurement protocol, the analyst, or the sample itself. These changes can create a deviation from...

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key terms and concepts

Learn how significance testing helps identify random versus determinate error, set hypotheses, and choose t-tests, F-tests, or Q-tests in chemistry.

Significance Testing in Analytical Chemistry | JoVE K12

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Significance Testing in Analytical Chemistry

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