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

K12 Standards

Texas Essential Knowledge and Skills

High School

Chemistry

CHEM.2

CHEM.2.C

Significance Testing: Overview

Czy różnica między tymi dwiema wartościami wynika z niewytłumaczalnego błędu losowego, czy błędu systematycznego, który można zracjonalizować za pomocą hipotetycznego modelu?
Test istotności to analiza statystyczna stosowana w celu sprawdzenia, czy różnicę między dwiema wartościami można wyjaśnić błędami nieokreślonymi, czy nie.
Hipoteza zerowa zakłada, że porównywane wartości są takie same, a wszelkie różnice wynikają z błędów nieokreślonych. Alternatywna hipoteza mówi, że różnica musi być rzeczywista i nie może być wyjaśniona przez nieokreślone błędy.
Poziom istotności oznaczony przez α określa warunek poziomu ufności dla trafności hipotezy zerowej. Hipoteza zerowa jest odrzucana, gdy wartości są obecne poza poziomem ufności.
Test istotności nazywa się jednostronnym, jeśli odrzucenie występuje tylko dla wartości na jednym końcu krzywej rozkładu normalnego. W dwustronnych testach istotności odrzucenie może wystąpić dla wartości przypadających na dowolny koniec krzywej rozkładu normalnego.

testowanie istotności: przegląd

Testowanie istotności to zestaw metod statystycznych używanych do sprawdzania, czy twierdzenie dotyczące parametru jest uzasadnione. W chemii analitycznej badanie istotności stosuje się przede wszystkim w celu ustalenia, czy różnica między dwiema wartościami wynika z błędów określonych czy losowych. Efekt konkretnej zmiany w protokole pomiaru, analityce lub samej próbce może spowodować odchylenie od oczekiwanego wyniku. W przypadku podejrzenia odchylenia/wartości odstającej musimy być w stanie potwierdzić matematycznie, że odchylenie pochodzi z określonego źródła i że obserwację z odchyleniem można w logiczny sposób pominąć w analizie.

Dwie hipotezy służą jako kryteria testowania istotności. Hipoteza zerowa (H0) stwierdza, że ​​porównywane wartości nie różnią się od siebie istotnie. Innymi słowy, jeśli istnieje jakakolwiek różnica między dwiema wartościami, przypisuje się ją nieokreślonemu błędowi. Hipoteza alternatywna (HA) stwierdza, że ​​porównywane wartości nie są równe, a różnica jest bardziej znacząca, niż można to wytłumaczyć błędem nieokreślonym.

Przed wykonaniem testu należy postawić hipotezy i ustalić poziom istotności (α). Następnie obliczana jest statystyka testowa, oparta na średniej próbki i odchyleniu standardowym, i porównywana z wartościami tabelarycznymi, które ustala się na poszczególnych poziomach istotności i definiuje jako jedno- lub dwustronne. Jeżeli obliczona statystyka testowa przekracza wartości krytyczne (statystyka tabelaryczna), hipoteza zerowa zostaje odrzucona i stwierdzamy, że różnicy między obiema wartościami nie można wytłumaczyć przypadkowym, nieokreślonym błędem.

W jednostronnym testowaniu istotności hipoteza alternatywna może określać, że obserwowana wartość jest wyższa lub niższa od wartości oczekiwanej, ale nie obie. W dwustronnym testowaniu istotności hipoteza alternatywna może po prostu stwierdzić, że obserwowana wartość nie jest równa wartości oczekiwanej, bez względu na kierunek.

Testowanie istotności można zastosować do różnych parametrów statystycznych jednego lub większej liczby zestawów danych. Testom nadaje się różne nazwy w zależności od parametrów lub celu. Testowanie istotności jest często stosowane w celu porównania zaobserwowanej wartości ze średnią lub porównania dwóch średnich z dwóch różnych zbiorów danych. Testy te nazywane są testami t. Testy istotności można również przeprowadzić na wariancji dwóch zbiorów danych. W tym przypadku test nazywany jest testem F. Jeżeli do identyfikacji wartości odstających stosuje się test istotności, nazywa się go testem Q.

Testowanie istotności: Przegląd

Czy różnica między tymi dwiema wartościami wynika z niewytłumaczalnego błędu losowego, czy błędu systematycznego, który można zracjonalizować za pomocą hipotetycznego modelu?
Ten...

Education

JoVE Core

Analytical Chemistry

Chemia analityczna

Unit 1

Jednostka 1

Chemical Applications of Statistical Analyses

Zastosowania analiz statystycznych w chemii

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

KLUCZOWE TERMINY I POJĘCIA

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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