“47.6% of statistics are made up on the spot.”

Even if they are nothing more than a bunch of numbers, data sets are supposed to be something sacred. Data sets tell us so much. They are at the heart of scientific progress: Nicolaus Copernicus’ observation of the heavens helped substantiate his heliocentric theory; clinical trials determine the viability of a potential cure or treatment; and large data sets on individuals can help pinpoint correlations between variables like wages and education. Every major study, every finding is backed by data, which represent new and future truths about the subject in question.

Data sets bear an important role and being able to collect, analyze and interpret the data properly is a necessity. In data collection, we were always told to make sure to have a representative sample. In data analysis, we were instructed to be aware of things like omitted variable bias or outliers. In our interpretation, we were asked to be wary of alternative explanations. And indeed, there is a very strict protocol to this long, long process of data excavation, preparation, and interpretation. Things can get ugly fast when the protocol is breached. By presenting misleading or faulty data, one can advocate for unsubstantiated claims. Combined with the power of social media, which can spread misinformation virally, these mistakes can yield irreparable damage.

Obviously, the data must be legitimate. This requirement has been stressed in every experimental anything I have ever done. My school teachers always warned us not to “fudge the data” just because the measurements we make didn’t correspond exactly with the theory. They told us that even the best experiments have some sort of error, but that does not stop us from making inferences. Nonetheless, it may sometimes be tempting – and certainly easier – to just make stuff up. As a student, academic, or professional researcher, there are a lot of things in our lives, and data collecting isn’t always a priority. One of my best friends faked his data in a psychology paper (because he waited until the last minute to complete the project) and, of course, he got a better mark than I did.

This leads us to the question of truth. If done well, fake data can be presented and seen as real data. Obviously, the psychology professor didn’t spend much time verifying the data, but one would have to imagine that researchers, unlike students, are held accountable, considering the enormous funding that goes into providing and collecting the data.

The issues of truth and ethics get harder when the data manipulation is deliberate, or systematic. For the past decade, economists have noticed a consistent discord with government data and private estimates. In 2013, Argentina became the first country to be censured by the International Monetary Fund (IMF) for showing inaccurate data. Since between the years 2004 and 2015 various statistics were misreported, such as inflation, GDP growth and currency valuation, the new government consequently revised the data. A report in Bloomberg noted that, at times, the numbers were staggeringly different, mentioning that in 2009 the economy contracted by 6% as opposed to a previously reported 0.1% growth. At the same time, the government reported a 70% inflation from 2007 to 2012; private companies reported an inflation of 200%. You can see why the previous government fudged the numbers.

These inaccuracies, though, have serious consequences for consumers and investors. The unreliability of statistics induces uncertainty into the economy, increasing the variance of a return of investment, for example. Interest rates would have to increase, because lenders have to charge a premium since the borrower might default, which hurts investors even more. Output would decrease, affecting real wages adversely. This cycle, exacerbated by the faulty data, seriously puts Argentina’s long-term potential growth in question. With the new government and help from the IMF, the goal is to provide reliable data and build trust in the economy again.

Yet even when numbers are not fabricated, we need to be very careful that we understand what they mean. For example, when we talk about the Gross Domestic Product, we need to be aware that the figure represents the output of the entire country; for increased accuracy and transparency, we would need to factor in a measure that considers the population size. A plausible measure would be GDP per capita. For example, according to the CIA Factbook, Luxembourg’s GDP is about 58 billion, which is ranked 107^th in the world. However, their GDP per capita (in PPP) is $102 000, making them the second wealthiest country on the list in per capita terms. The latter number better reflects the reality in Luxembourg but we must be aware that the per capita measure is an average of the population. This means that a handful of rich people will skew the data, making poorer people appear better off than they really are. The United States boasts 540 billionaires according to Forbes, and this figure will certainly affect USA’s impressive position as the 18^th wealthiest country in terms of GDP per capita.

This idea may seem obvious to some, but the fact that data has so much to hide is very subtle and massively under-disclosed on a daily basis. For example, during the transition of power in the recent US election, one of Trump’s major talking points was Barack Obama’s “disastrous” effect on unemployment. According to CNN, the United States had a 4.9% unemployment rate for last October, which was below the target threshold. Normatively, this number is very strong but, as always, we should be wary of the statistic. The Bureau of Labour Statistics, which publishes these data, rigorously defines who counts as employed, unemployed, and not in the labour force. The unemployment rate is the rate of the employed among the labour force. However, a person needs to be looking for a job to be considered a part of the labour force. If someone were not employed, but hasn’t looked for a job in the past few months, he or she would be removed from the labour force and omitted in the calculation. As a result, other measures such as labour force size, employment-population ratio, or the labour force participation rate offer important comparisons. And even then, we should consider the quality of the jobs to see if the workers are well off, especially where underemployment (where a worker is technically employed, but with either very low wages or extremely part-time work) is concerned.

In short, even though most of the time the numbers are all there, interpretations of those numbers can tell a different story, which calls into question whether there exists a concrete, definable truth in data. Of course, this brings up the very delicate, endlessly debated philosophical questions surrounding “truth” but, at the end of the day, statistics and data are meant to find relationships or trends, nothing more.