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

2つの値の差は、説明できないランダムエラーによるものなのか、それとも仮想モデルによって合理化できる系統誤差によるものなのか?
有意性検定は、2つの値の違いが不確定なエラーによって説明できるかどうかを検証するために使用される統計分析です。
帰無仮説は、比較された値が同じであり、違いはすべて不確定なエラーに起因すると仮定します。対立仮説は、差は実数でなければならず、不確定な誤差では説明できないと述べています。
αで示される有意水準は、帰無仮説の有効性の信頼水準条件を設定します。帰無仮説は、信頼水準を超える値が存在する場合に棄却されます。
有意性検定は、正規分布曲線の一方の端のみの値に対して棄却が発生する場合、片側検定と呼ばれます。両側有意性検定では、正規分布曲線の両端にある値に対して棄却が発生する可能性があります。

有意性テスト: 概要

有意性テストは、パラメータに関する主張が有効かどうかをテストするために使用される一連の統計的手法です。分析化学では、有意性検定は主に、2 つの値の差が確定誤差によるものかランダムな誤差によるものかを判断するために使用されます。測定プロトコル、分析者、またはサンプル自体の特定の変更の影響により、予想される結果からの逸脱が生じる可能性があります。偏差/外れ値が疑われる場合は、その偏差が明確な原因から来ていること、および偏差のある観測値を分析から論理的に除外できることを数学的に確認できる必要があります。

2 つの仮説が有意性検定の基準として使用されます。帰無仮説 (H0) は、比較される値が互いに大きく異ならないことを示す。言い換えれば、2 つの値の間に差異が存在する場合、それは不定誤差に起因すると考えられます。対立仮説 (HA) は、比較された値が等しくなく、その差が不定誤差で説明できるよりも重大であることを示す。

検定を実行する前に、仮説を述べ、有意水準 (α) を設定する必要があります。次に、サンプル平均と標準偏差に基づく検定統計量が計算され、特定の有意水準に設定され、片側または両側として定義される表の値と比較されます。計算された検定統計量が臨界値 (表形式の統計量) を超える場合、帰無仮説は棄却され、2 つの値の差はランダムな不確定誤差では説明できないと述べます。

片側有意性検定では、対立仮説により、観測値が期待値より高いか低いかのいずれかが指定できますが、両方は指定できません。両側有意性検定では、対立仮説は、方向に関係なく、観測値が期待値と等しくないことを単に述べることができます。

有意性検定は、1 つ以上のデータセットのさまざまな統計パラメータに対して使用できます。テストにはパラメータや目的に応じて異なる名前が付けられます。有意性検定は、観測値と平均値を比較したり、2 つの異なるデータセットの 2 つの平均値を比較したりするために頻繁に適用されます。これらの検定は t 検定として知られています。 2 つのデータセットの分散に対して有意性検定を実行することもできます。この場合、このテストは F テストと呼ばれます。有意性検定を使用して外れ値を特定する場合、その検定は Q 検定と呼ばれます。

2つの値の差は、説明できないランダムエラーによるものなのか、それとも仮想モデルによって合理化できる系統誤差によるものなのか?
ザ。。。

Education

JoVE Core

Analytical Chemistry

分析化学

Unit 1

ユニット1

Chemical Applications of Statistical Analyses

統計分析の化学的応用

chemical-applications-of-statistical-analyses

KEY TERMS AND CONCEPTS

主要用語と概念

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

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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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