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

İki değer arasındaki fark, açıklanamayan rastgele bir hatadan mı yoksa varsayımsal bir modelle rasyonelleştirilebilen sistematik bir hatadan mı kaynaklanıyor?
Anlamlılık testi, iki değer arasındaki farkın belirsiz hatalarla açıklanıp açıklanamayacağını doğrulamak için kullanılan istatistiksel bir analizdir.
Sıfır hipotezi, karşılaştırılan değerlerin aynı olduğunu ve herhangi bir farkın belirsiz hatalardan kaynaklandığını varsayar. Alternatif hipotez, farkın gerçek olması gerektiğini ve belirsiz hatalarla açıklanamayacağını belirtir.
α ile gösterilen bir anlamlılık düzeyi, sıfır hipotezinin geçerliliği için bir güven düzeyi koşulu belirler. Sıfır hipotezi, değerler güven düzeyinin ötesinde mevcut olduğunda reddedilir.
Anlamlılık testi, normal dağılım eğrisinin yalnızca bir ucundaki değerler için reddetme meydana gelirse tek kuyruklu olarak adlandırılır. İki kuyruklu anlamlılık testlerinde, normal dağılım eğrisinin her iki ucuna düşen değerler için reddetme meydana gelebilir.

anlamlılık testi: genel bakış

Anlamlılık testi, bir parametre hakkındaki bir iddianın geçerli olup olmadığını test etmek için kullanılan bir dizi istatistiksel yöntemdir. Analitik kimyada anlamlılık testi öncelikle iki değer arasındaki farkın belirli hatalardan mı yoksa rastgele hatalardan mı kaynaklandığını belirlemek için kullanılır. Ölçüm protokolünde, analistte veya numunenin kendisinde yapılan belirli bir değişikliğin etkisi, beklenen sonuçtan sapmaya neden olabilir. Şüpheli bir sapma/aykırı değer durumunda, sapmanın belirli bir kaynaktan geldiğini ve sapmayla birlikte gözlemin mantıksal olarak analizden çıkarılabileceğini matematiksel olarak doğrulayabilmemiz gerekir.

Anlamlılık testi için kriter olarak iki hipotez kullanılır. Sıfır hipotezi (H0), karşılaştırılan değerlerin birbirinden önemli ölçüde farklı olmadığını belirtir. Yani iki değer arasında fark varsa bu belirsiz bir hataya atfedilir. Alternatif hipotez (HA), karşılaştırılan değerlerin eşit olmadığını ve farkın belirsiz hatayla açıklanabilecekten daha önemli olduğunu belirtir.

Test yapılmadan önce hipotezlerin belirlenmesi ve bir anlamlılık düzeyinin (α) belirlenmesi gerekir. Daha sonra numune ortalamasına ve standart sapmaya dayalı test istatistiği hesaplanır ve belirli anlamlılık seviyelerinde belirlenen ve bir veya iki kuyruklu olarak tanımlanan tablo halindeki değerlerle karşılaştırılır. Hesaplanan test istatistiğinin kritik değerleri (tablolu istatistik) aşması durumunda sıfır hipotezi reddedilir ve iki değer arasındaki farkın rastgele, belirsiz hatayla açıklanamayacağını belirtiriz.

Tek kuyruklu anlamlılık testinde alternatif hipotez, gözlemlenen değerin beklenen değerden daha yüksek veya daha düşük olduğunu belirtebilir ancak her ikisini birden belirtemez. İki kuyruklu anlamlılık testinde alternatif hipotez, yöne bakılmaksızın gözlemlenen değerin beklenen değere eşit olmadığını basitçe ifade edebilir.

Anlamlılık testi, bir veya daha fazla veri setinin farklı istatistiksel parametreleri üzerinde kullanılabilir. Testlere parametrelere veya amaca bağlı olarak farklı isimler verilir. Anlamlılık testi, gözlemlenen bir değeri ortalamayla karşılaştırmak veya iki farklı veri setinden iki ortalamayı karşılaştırmak için sıklıkla uygulanır. Bu testler t-testleri olarak bilinir. İki veri setinin varyansı üzerinde de anlamlılık testleri yapılabilir. Bu durumda teste F testi adı verilir. Aykırı değerleri belirlemek için bir anlamlılık testi kullanılıyorsa, teste Q testi adı verilir.

Anlamlılık Testi: Genel Bakış

İki değer arasındaki fark, açıklanamayan rastgele bir hatadan mı yoksa varsayımsal bir modelle rasyonelleştirilebilen sistematik bir hatadan mı kaynaklanıyor?
Bu ...

Education

JoVE Core

Analytical Chemistry

Analitik Kimya

Unit 1

Ünite 1

Chemical Applications of Statistical Analyses

İstatistiksel Analizlerin Kimyasal Uygulamaları

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

TEMEL TERİMLER VE KAVRAMLAR

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

si units: 2019 redefinition

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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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Comparing Variance with the F-Test

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Student's t-Test for Significance Checks

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Interpreting Correlation in Calibration Curves

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quantifying and rejecting outliers: the grubbs test

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Grubbs Test for Spotting Data Outliers

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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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