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

CHEM.2

CHEM.2.C

Significance Testing: Overview

Is the difference between the two values due to an unexplainable random error or a systematic error that can be rationalized by a hypothetical model?
The significance test is a statistical analysis used to validate whether the difference between two values can be explained by indeterminate errors or not.
The null hypothesis assumes that the values compared are the same and any difference stems from indeterminate errors. The alternate hypothesis states that the difference must be real and cannot be explained by indeterminate errors.
A significance level denoted by &#945; sets a confidence level condition for the validity of the null hypothesis. The null hypothesis is rejected when values are present beyond the confidence level.
The significance test is called one-tailed if rejection occurs for values at only one end of the normal distribution curve. In two-tailed significance tests, the rejection can occur for values falling at either end of the normal distribution curve.

significance testing: overview

Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically that the deviation comes from a determinate source and that the observation with the deviation can be logically omitted from the analysis.
Two hypotheses are used as criteria for significance testing. The null hypothesis (H0) states that the values being compared do not differ from each other significantly. In other words, if any difference exists between two values, it is ascribed to an indeterminate error. The alternate hypothesis (HA) states that the compared values are not equal, and the difference is more significant than can be explained by indeterminate error.
Before the test is performed, the hypotheses need to be stated, and a significance level (&#945;) needs to be set. The test statistic, based on the sample mean and standard deviation, is then calculated and compared to the tabulated values, which are set at particular significance levels and defined as one- or two-tailed. If the calculated test statistic exceeds the critical values (tabulated statistic), the null hypothesis is rejected, and we state that the difference between the two values cannot be explained by random, indeterminate error.
In one-tailed significance testing, the alternative hypothesis can specify that the observed value is either higher or lower than the expected value, but not both. In two-tailed significance testing, the alternative hypothesis can simply state that the observed value is not equal to the expected value, with no regard to the direction.
Significance testing can be used on different statistical parameters of one or more data sets. Tests are given different names depending on the parameters or purpose. Significance testing is frequently applied to compare an observed value with the mean or compare two means from two different data sets. These tests are known as t-tests. Significance tests can also be performed on the variance of two data sets. In this case, the test is known as an F-test. If a significance test is used to identify outliers, the test is called a Q-test.

Is the difference between the two values due to an unexplainable random error or a systematic error that can be rationalized by a hypothetical model?
The ...

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Analytical Chemistry

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Chemical Applications of Statistical Analyses

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KEY TERMS AND CONCEPTS

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 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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