Data, whether big or small, can be quite confusing. It is like we are blindfolded and feeling an elephant. We all describe it differently based on what we touch. Data analysts also show different sides of the data and sometimes they say opposite things. This divergence is not limited to scientific studies but extends to socioeconomic data analysis, and it has real-world implications.
Consider a scientific example. A study in the British Journal of Anaesthesia in 2019 found no link between higher anesthetic doses and early patient deaths among older patients. Another study in the same journal in the same year, using the same data, concluded differently. It claimed that the data was insufficient to draw any conclusions about mortality. This happens more often than you might think. For example, 246 biologists studied the same ecological data and came up with very different findings.
This is not limited to science. It happens with economic and social data too. For instance, in the 2016 U.S. presidential election in Florida, one survey said Hillary Clinton was 1 percent ahead of Donald Trump. But when a few other experts used the same data to make predictions, their forecasts were 5 percent apart. It is surprising how experts, working with the same data, can differ in conclusions.
These discrepancies can lead to controversies and challenges when shaping policies, strategies or frameworks based on data analysis. This was seen during the Covid-19 pandemic when the estimates and projections for infection rates, deaths and job losses differed even though same data was used.
As the world began to recover from the pandemic, experts used the same data to conclude whether an economy was recovering in a V-, W-, or K-shaped fashion. It is challenging for both experts and the public to understand why different analysts reach contradictory conclusions and who to trust.
