Whenever we make an assumption or pose a hypothesis, it’s essential to verify its validity. Let’s take a scenario: a new drug, “X,” is developed to treat disease “Y” and has shown positive results in curing “N” number of patients. The immediate impulse might be to start mass-producing and advertising the drug, touting its effectiveness. However, there’s an essential step to consider first: hypothesis testing.
Scientific assumptions and hypotheses differ from mathematical proofs. While a mathematical proof deals with certainties, scientific experiments typically deal with samples rather than entire populations. This introduces potential errors. In our drug example, the fact that the drug was effective on a sample group doesn’t guarantee its effectiveness on the entire global population. There might be variations due to genetics, environment, or other factors that weren’t present in the initial sample.