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

A diferença entre os dois valores se deve a um erro aleatório inexplicável ou a um erro sistemático que pode ser racionalizado por um modelo hipotético?
O teste de significância é uma análise estatística utilizada para validar se a diferença entre dois valores pode ser explicada por erros indeterminados ou não.
A hipótese nula pressupõe que os valores comparados são os mesmos e qualquer diferença decorre de erros indeterminados. A hipótese alternativa afirma que a diferença deve ser real e não pode ser explicada por erros indeterminados.
Um nível de significância denotado por α define uma condição de nível de confiança para a validade da hipótese nula. A hipótese nula é rejeitada quando os valores estão presentes além do nível de confiança.
O teste de significância é chamado unicaudal se a rejeição ocorrer para valores em apenas uma extremidade da curva de distribuição normal. Em testes de significância bicaudal, a rejeição pode ocorrer para valores que caem em qualquer extremidade da curva de distribuição normal.

teste de significância: visão geral

O teste de significância é um conjunto de métodos estatísticos usados ​​para testar se uma afirmação sobre um parâmetro é válida. Na química analítica, o teste de significância é usado principalmente para determinar se a diferença entre dois valores vem de erros determinados ou aleatórios. O efeito de uma alteração específica no protocolo de medição, no analista ou na própria amostra pode causar um desvio do resultado esperado. No caso de uma suspeita de desvio/valor atípico, precisamos ser capazes de confirmar matematicamente que o desvio vem de uma fonte determinada e que a observação com o desvio pode ser logicamente omitida da análise.

Duas hipóteses é usadas como critérios para testes de significância. A hipótese nula (H0) afirma que os valores comparados não diferem significativamente entre si. Em outras palavras, se existir alguma diferença entre dois valores, ela será atribuída a um erro indeterminado. A hipótese alternativa (HA) afirma que os valores comparados não é iguais e a diferença é mais significativa do que pode ser explicada pelo erro indeterminado.

Antes de o teste ser realizado, as hipóteses precisam ser declaradas e um nível de significância (α) precisa ser definido. A estatística de teste, com base na média amostral e no desvio padrão, é então calculada e comparada com os valores tabulados, que é definidos em níveis de significância específicos e definidos como unicaudais ou bicaudais. Se a estatística de teste calculada exceder os valores críticos (estatística tabulada), a hipótese nula é rejeitada e afirmamos que a diferença entre os dois valores não pode ser explicada por erro aleatório e indeterminado.

No teste de significância unicaudal, a hipótese alternativa pode especificar que o valor observado é superior ou inferior ao valor esperado, mas não ambos. No teste de significância bicaudal, a hipótese alternativa pode simplesmente afirmar que o valor observado não é igual ao valor esperado, independentemente da direção.

O teste de significância pode ser usado em diferentes parâmetros estatísticos de um ou mais conjuntos de dados. Os testes recebem nomes diferentes dependendo dos parâmetros ou finalidade. O teste de significância é frequentemente aplicado para comparar um valor observado com a média ou comparar duas médias de dois conjuntos de dados diferentes. Esses testes é conhecidos como testes t. Testes de significância também podem ser realizados na variância de dois conjuntos de dados. Nesse caso, o teste é conhecido como teste F. Se um teste de significância for usado para identificar valores atípicos, o teste será chamado de teste Q.

Teste de Significância: Visão Geral

A diferença entre os dois valores se deve a um erro aleatório inexplicável ou a um erro sistemático que pode ser racionalizado por um modelo hipotético?
O...

Education

JoVE Core

Analytical Chemistry

Química Analítica

Unit 1

Unidade 1

Chemical Applications of Statistical Analyses

Aplicações Químicas de Análises Estatísticas

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

TERMOS E CONCEITOS-CHAVE

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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