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

두 값 간의 차이는 설명할 수 없는 무작위 오류 때문입니까, 아니면 가상 모델로 합리화할 수 있는 체계적 오류 때문입니까?
유의성 검정은 두 값 간의 차이를 불확정 오류로 설명할 수 있는지 여부를 검증하는 데 사용되는 통계 분석입니다.
귀무 가설은 비교된 값이 동일하고 모든 차이가 불확실한 오류에서 비롯된다고 가정합니다. 대립 가설은 차이가 실재해야 하며 불확정 오류로 설명할 수 없다고 말합니다.
α로 표시되는 유의 수준은 귀무 가설의 타당성에 대한 신뢰 수준 조건을 설정합니다. 귀무 가설은 값이 신뢰 수준을 벗어나 존재할 때 기각됩니다.
유의 검정은 정규 분포 곡선의 한쪽 끝에만 있는 값에 대해서만 거부가 발생하는 경우 단측 검정이라고 합니다. 양측 유의성 검정에서는 정규 분포 곡선의 양쪽 끝에 있는 값에 대해 기각이 발생할 수 있습니다.

유의성 검사: 개요

유의성 검사는 매개변수에 대한 주장이 유효한지 여부를 검사하는 데 사용되는 일련의 통계적 방법입니다. 분석 화학에서 유의성 검사는 주로 두 값 사이의 차이가 확정 오류에서 비롯되는지 무작위 오류에서 비롯되는지 확인하는 데 사용됩니다. 측정 프로토콜, 분석가 또는 샘플 자체의 특정 변경으로 인해 예상 결과와 편차가 발생할 수 있습니다. 편차/이상값이 의심되는 경우 편차가 특정 소스에서 발생하고 편차가 있는 관찰이 분석에서 논리적으로 생략될 수 있음을 수학적으로 확인할 수 있어야 합니다.

두 가지 가설이 유의성 검사의 기준으로 사용됩니다. 귀무가설(H0)은 비교되는 값이 서로 크게 다르지 않다는 것입니다. 즉, 두 값 사이에 차이가 있으면 불확실한 오류로 간주됩니다. 대립가설(HA)은 비교된 값이 동일하지 않으며 그 차이가 불확실한 오류로 설명할 수 있는 것보다 더 크다는 것입니다.

검정을 수행하기 전에 가설을 설정하고 유의수준(α)을 설정해야 합니다. 그런 다음 표본 평균과 표준 편차를 기반으로 한 검정 통계량이 계산되어 특정 유의 수준으로 설정되고 단측 또는 양측으로 정의된 표 값과 비교됩니다. 계산된 검정 통계량이 임계값(통계표)을 초과하면 귀무가설이 기각되고 두 값의 차이는 무작위, 불확정 오류로 설명될 수 없다고 명시합니다.

단측 유의성 검정에서 대립 가설은 관측값이 기대값보다 높거나 낮지만 둘 다일 수는 없다는 것을 지정할 수 있습니다. 양측 유의성 검정에서 대립 가설은 방향에 관계없이 관측값이 기대값과 동일하지 않다고 간단하게 진술할 수 있습니다.

하나 이상의 자료 세트의 다양한 통계 매개변수에 대해 유의성 검사를 사용할 수 있습니다. 검사에는 매개변수나 목적에 따라 다른 이름이 지정됩니다. 관찰된 값을 평균과 비교하거나 서로 다른 두 자료 세트의 두 평균을 비교하기 위해 유의성 검사가 자주 적용됩니다. 이러한 검사를 t-검사라고 합니다. 두 자료 세트의 분산에 대해 유의성 검사를 수행할 수도 있습니다. 이 경우 검사를 F-검사라고 합니다. 이상값을 식별하기 위해 유의성 검사를 사용하는 경우 해당 검사를 Q-검사라고 합니다.

두 값 간의 차이는 설명할 수 없는 무작위 오류 때문입니까, 아니면 가상 모델로 합리화할 수 있는 체계적 오류 때문입니까?
그만큼 ...

Education

교육

JoVE Core

Analytical Chemistry

분석 화학

Unit 1

제1단원

Chemical Applications of Statistical Analyses

통계 분석의 화학적 응용

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

주요 용어 및 개념

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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