Understanding data goes beyond merely loading it into Excel or visualising it in Tableau. It involves comprehending correlation, causation and drivers, as well as interpreting it accurately. Data interpretation implies grasping any trend or effect of specific drivers. In medicine, data is frequently employed to understand the impact of certain treatments on curing particular ailments. Let's consider a large pharmaceutical company studying the effect of two drugs on the treatment of a particular disease. The company discovers that drug B boasts an 83% success rate in treating the disease (289/350), while drug A has a success rate of 78% (273/350). Clearly, the company would advocate for drug B as it seems to exhibit a higher success rate. However, suppose a savvy analyst decides to delve deeper and divides the population into those with a mild version of the disease and those with a severe case.
Heads up, there's a mix-up in the stats about the drugs. Drug A actually has a higher success rate (83%) compared to drug B (78%). The text mistakenly says it's the other way around. Just a quick fix needed I think!
Heads up, there's a mix-up in the stats about the drugs. Drug A actually has a higher success rate (83%) compared to drug B (78%). The text mistakenly says it's the other way around. Just a quick fix needed I think!