5. Handling False Negatives and Positives

Sometimes, we are talking about sample losses. There is also another aspect to sample losses lead to underestimation, which is usually falls under the category of false negative, but there is also another case of false positives, which means that what we are calling us false negative is not just whether it is not a true false answer. We are saying false negative essentially means there is an underestimation. It is another way of representing a false negative that is you are assuming that something is not there when it is there.

A false negative occurs when a test fails to detect a substance that is actually present. For example, if a water sample is tested for a contaminant and the test results indicate that the contaminant is not present, even though it actually is, that is a false negative. This kind of mistake is especially concerning in environmental monitoring because decisions may be made based on these incorrect results.

Think of a fire alarm that doesn't go off when there is smoke in a room. The smoke is like a contaminant in the water sample. If the alarm (the test) fails to sound, people might assume there is no fire (that the contaminant isn't there), leading to a dangerous situation.

Similarly, you have false positive, which means you are overestimating and this can happen if you have sample gain. So sample gain it seems not intuitive because sample will lose, where can you gain sample from since mass cannot be created from nothing. So there are a few instances where you get sample gain and the sample gain happens, the sample gain we are talking about mean by addition of the analyte from somewhere.

A false positive occurs when a test incorrectly indicates the presence of a substance that isn’t there. In environmental analysis, this can happen due to sample gain, where contaminants from outside sources, like dirty glassware or contaminated instruments, inadvertently alter the sample's concentration. This leads to an overestimation of contaminant levels.

Imagine you are testing soil for a specific nutrient. If you used a shovel that was previously used to dig in a fertilized area without cleaning it, you might bring some of that fertilizer into your new sample. This results in a false positive, where it seems like the new soil has a higher nutrient level than it actually does.

Addition of analyte in the sample, not at the collection site but somewhere else by an artificial mean. So how does this happen? This happens by several means. One is the most common contaminated apparatus. This is a very general case. Contaminated apparatus very simply it means dirty glassware or containers, dirty transfer equipment.

Sample gain can occur when the equipment used in the analysis introduces contaminants unintentionally. For example, if glassware or transfer equipment is not properly cleaned, residual materials can mix with the samples, leading to erroneous high readings in contaminant levels.

Consider cooking a recipe that calls for fresh ingredients. If you use a pot that still has residue from a previous dish without cleaning it, the dish might end up tasting completely different than intended. Similarly, dirty equipment can affect the results of the water or soil samples being tested.

So, how do you check for this? The checking for this is why we use what is called as blanks and we have discussed this in other lecture, but the blanks analysis is very important...

To identify and mitigate false positive results, laboratories use blank samples, which are uncontaminated samples that undergo the same analysis as actual samples. By analyzing these blanks, one can determine if there are any contaminants introduced by the equipment or the environment. If the blank shows a concentration above zero, it indicates contamination in the equipment or process.

Think of a control group in a medical experiment. A control group does not receive the treatment being tested, allowing researchers to see how many conditions improved without intervention. If a control group experiences improvements, it indicates that other factors in the environment (like contamination) may influence results.

So, to summarize we need to estimate recovery for losses, we need to do blank analysis for sample gains. Sample gain can also happen by contamination of the sample itself.

To ensure accurate measurements in environmental analyses, recovery rates need to be established, which indicate how much of the analyte is actually recovered from the sample after processing. Additionally, systematic blank analyses help to assess contamination, ensuring that any detected samples truly represent the state of the environment rather than artifacts of the testing process.

Imagine checking your fuel efficiency in a car. To get an accurate measurement of how far you can go on a tank of gas, you need to account for variables like driving conditions or whether the car is weighted down. Just like conducting controls (blanks) in sampling helps remove extraneous factors to ensure accuracy in test results.

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