Absence of evidence is not evidence of absence #worldresearchaward #hypothesis #BRCA gene mutation

 




Researchers in all kinds of fields use the scientific method to investigate the questions they’re interested in.

First, a scientist formulates a new claim – what’s called a hypothesis. For example, is having some genetic mutations in BRCA genes related to a higher risk of breast cancer? Then they gather data relevant to the hypothesis and decide, based on the data, whether that initial claim was correct or not.

It’s intuitive to think that this decision is cleanly dichotomous – that the researcher decides the hypothesis is either true or false. But of course, just because you decide something doesn’t mean you’re right.If the claim is really false but the researcher decides, based on the evidence, it’s true – a false positive – they commit what’s called a Type 1 error. If the claim is really true but the researcher fails to see that – a false-negative conclusion – then they commit a Type 2 error.

Moreover, in the real world, it gets a little messier. It’s really hard to decide about the truth or falsity of a claim just based on what’s observed.

For that reason, most scientists employ what is called the null hypothesis significance testing framework. Here’s how it works: A researcher first states a “null hypothesis,” something that’s contrary to what they want to prove. For instance, in our example the null hypothesis is that BRCA genetic mutations are not associated with increased breast cancer occurrence.

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