Analytics

Analytics are both misunderstood by its proponents and its detractors. Statistics are not ever wrong, but are often used to measure something that the statistics do not apply to. For instance, GDP does measure the monetary output of all the goods and services of the country. However, it does not account for a) measuring inflation (i.e. the GDP of a country can look vastly larger if the currency that GDP is measured in goes down significantly in value) and b) price drops (i.e. the price of a good may fall significantly due to increased supply. While this does not measure immediately in GDP, overall the standard of living has gone up for citizens). Statistics are often used to mislead people as well. For instance, GDP growth may be used to support the re-election of a candidate, despite the fact that economic cycles are virtually agnostic to whom is elected or not (look at the stock market; you will not be able to decipher long term trends based on who was elected). This misleading use of statistics is often flaunted in the media, as most news websites do not have the time to explain them and most journalists do not understand them. However, detractors of analytics also miss a few key points. First, most detractors of analytics do not understand math, and therefore have necessary incentive to go after numbers indiscriminately, because for all they know, the numbers are the same. Second, analytics can be used if questions are asked in the correct way in every context that they apply to, including the ones where many people say that they should not apply. For instance, if there was an economic report that suggested that rich would help the economy more by keeping their own money rather than giving it to charity, this is a conclusion that everyone should seriously consider, despite possible backlashes against greed. These are just some of the examples of misunderstanding on all sides of the argument.