I wouldn't speak for everyone but in my surroundings people don't conclude anything from a single experiment. In molecular biology, experiments are done several times, as a rule, but what's more, different experiments are done to corroborate one point.

For example, when a biologist looks for a certain gene in a certain cell line, he checks for RNA (e.g. qPCR), then for protein (e.g. western blot), then for specific localization of the gene product (e.g. immunostaining). Then, if all of the data fit together, the scientist can conclude - okay, this gene is there and it seems to work. You don't do one qPCR run and then "conclude" that, oh, the gene is/isn't there.

I wouldn't speak for everyone but in my surroundings people don't conclude anything from a single experiment. In molecular biology, experiments are done several times, as a rule, but what's more, different experiments are done to corroborate one point.

For example, when a biologist looks for a certain gene in a certain cell line, he checks for RNA (e.g. qPCR), then for protein (e.g. western blot), then for specific localization of the gene product (e.g. immunostaining). Then, if all of the data fit together, the scientist can conclude - okay, this gene is there and it seems to work. You don't do one qPCR run and then "conclude" that, oh, the gene is/isn't there.

They are more susceptible to false positives when they want to rule in an association and to false negatives when they want to rule out an association. Since most experiments are aimed to rule in, the scientific community demands smaller type I error (false positives) than type II error (false negative) to counter balance such tendency (also because mis-information may be worse than missed information).

As we know that while standardizing or planning an experiment, we should have two replicates; 1. Experimental rep and 2. Biological rep. In this scenario, the chance of selecting false negatives and false positives may be less. When considering the effect of the two, both might have equal social, economical and health impacts. Susceptibility depends on two things; result oriented or research oriented.

Here are some thoughts about the topic,

Generally speaking statistical significance testing aims to reduce type I error = false positives more stringently than type II error = false negatives. For example in drug testing it is more safe to set inclusion criteria so high, that false positives are all ruled out and consequently some false negatives are included, i.e. real positives are being dropped out.

For example in our lab when performing ELISA, using positive control samples we set the threshold for positive so high, that we should not get false positives. By doing this, we will likely fail to detect some real positives, which were left under the threshold and so labeled as negative. We emphasize sensitivity more than specificity.

It also depends what is the effect size one wants to see. If the effect is modest and variability is great, the more samples/testing is needed to see the difference. Notable difference is more easily detected. Therefore small differences are more likely to be ignored.

Also in criminal justice cases it is more severe to jail an innocent person = false positive, than let the guilty walk free = false negative.

Doctors also are more likely to over treat healthy individuals = false negative, than left truly ill patient without care, possibly leading to death =false positive.

I work with experimental lab work.

Dear Adron,

I always remember the "guilty-not guilty" verdict: Type II (false negative) would produce an innocent verdict for a guilty person; whereas Type I (false positive) would result in an innocent party being found guilty. In the latter case, not only we send to jail an innocent, but we let loose the guilty one(s). In the former case, we (only) let loose the guilty one(s).

For a long time, and in many areas, 5% for Type I and 20% for Type II was considered as an unwritten rule. Nowadays things changed, in many domains, but there are still published papers giving a 4:1 ratio between the two types of errors.

A false positive is an error in data reporting in which a test result incorrectly indicates the presence of a condition such as a disease when the disease is not present, while a false negative is the opposite error where the test result incorrectly fails to indicate the presence of a condition when it is present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result, a true positive and a true negative.) They are also known in medicine as a false positive (respectively negative) diagnosis, and in statistical classification as a false positive (respectively negative) error.

https://en.m.wikipedia.org/wiki/False_positives_and_false_negatives

A false positive error, or false positive, is a result that indicates a given condition exists when it does not. For example, a pregnancy test which indicates a woman is pregnant when she is not, or the conviction of an innocent person.

A false positive error is a type I error where the test is checking a single condition, and wrongly gives an affirmative (positive) decision. However it is important to distinguish between the type 1 error rate and the probability of a positive result being false. The latter is known as the false positive risk https://en.m.wikipedia.org/wiki/False_positives_and_false_negatives