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You came up hot for marijuana on a screening test then were negative on a confirmation test, right? It's got a lot to do with the way they run the tests.
Screening tests are quick, inexpensive tests given to pick out all the people who might have been using drugs. These catch a lot of people who were taking drugs that aren't pot but kinda look like pot to the testing device--Advil is one drug that looks like pot, and poppyseeds are famous for looking like heroin. Drug test dip sticks, EMIT, and ELISA tests are screening tests.
Confirmation tests are very accurate, more-expensive tests that determine exactly what it is you're taking. The most popular confirmation test is "gas chromatograph/mass spectrometer" or GC/MS.
It's not unheard of for a whole box of screened-positive samples to test negative on a GC/MS. The reason they run the screening test is to keep the other 19 boxes of samples, which ALL screened negative, from being run through the expensive machine.
What else can I help you with?
A person who is concerned with practical results?
A pragmatic person could be a person who is concerned with practical results.
Is it possible for two sample sets to have the same range but different means?
could a sample set have the same range but different means
What is person who asks for handouts?
could be someone who wants a free sample, could be food given to a beggar
What a person who ask for handouts?
could be someone who wants a free sample, could be food given to a beggar
Why is sample size important in determining probability?
The bigger the sample size the more accurate the results will be. For example, if you roll a 6 sided die and track the results to get the probability of rolling a six. If you only roll 6 times, then you may not even get one 6 or you could get a few. A small sample size means you won't get very reliable results.
When a sample could misrepresent the validity of the data?
A sample could misrepresent the validity of the data when it is not representative of the larger population, leading to biased results. This can occur due to sampling errors, such as selection bias, where certain groups are overrepresented or underrepresented. Additionally, a small sample size may increase the variability of the results, making it difficult to generalize findings. Consequently, using a poorly chosen sample can lead to incorrect conclusions and undermine the reliability of the study.
Why is a sample better than a census?
its better because we often don't have to survey a large population, so a sample is quicker, easier, requires few ressources, little time and can be more accurate if a person is not there to answer it because a sample could represent that person.
Person who asks for handouts?
could be someone who wants a free sample, could be food given to a beggar
Could a water sample have a high concentration of the pathogenic bacterium vibrio cholerae and give negative results in the multiple-tube technique?
Yes, a water sample could have a high concentration of Vibrio cholerae but give negative results in the multiple-tube technique if the bacteria are unevenly distributed in the sample. The technique relies on statistical probability and multiple dilutions to estimate bacterial concentrations, so if the samples taken from the dilution series do not contain the bacteria, the results can be falsely negative.
In statistics which is better large sample size or small sample size?
Generally, the larger the sample the more reliable the results. Example: If you flipped a coin twice and got heads both times you could say the coined is biased towards heads. However, if you repeat the experiment 100 times your results will be a lot more reliable.
How would a contaminated unknown sample affect the accurate identification of the unknown?
A contaminated unknown sample could potentially introduce foreign material that may interfere with the identification process. This contamination could lead to misleading results or hinder the ability to correctly identify the unknown sample. It is important to ensure the sample is pure and free from contamination for accurate identification.
Why do we have to filter the food sample in experiment?
Filtering the food sample in an experiment helps remove any solid particles or impurities that could interfere with the analysis or measurements being conducted. It ensures that the sample is in a homogenous and consistent form for accurate testing and results.
