Statistical Errors and Effects

Analytical follies can reduce the effectiveness of using statistics to back up a specific stance. The statistical threshold of alpha=0.05 is frequently used to determine if a result is significant or non-significant. If a p-value is less than the threshold, it is considered statistically significant. While this is useful information in disproving a hypothesis, it cannot be the sole evidence (Makin & Xivry, 2019). Another factor that can lead to errors is interpreting correlation as causation. The correlation of two highly correlated variables could be caused by coincidence, reverse-causation, or an unknown cause entirely  (Makin & Xivry, 2019). To prevent these errors in statistical analysis skewing results, more tests should be performed to gather additional evidence.

As an example of a statistical error, experts predicted that a rapid increase in crime rates would occur in the early to mid-1990s. These predictions were based on a pattern of continuous growth in crime that had been observed through the 1980s (Levitt, 2004). Instead, crime rates began to fall rather than increase, and they generally continue to fall through the present day. In this case, errors in statistical analysis resulted in a failure to predict the actual change in crime rate. The statistical analysis did not consider factors such as an increased police presence, an aging population, receding drug usage, and the passing of laws that resulted in a smaller at-risk population (Levitt, 2004). A reduction in crime is obviously a positive thing, but the statistical error resulted in the public searching for answers as to why the crime rate dropped rather than increased, resulting in incorrect and often harmful conclusions.

Many felt that the drop in crime was due to innovative policing strategies such as stop-and-frisk. This led to the public calling for the program’s expansion in areas with higher crime such as New York City and Chicago (Levitt, 2004). In reality, these types of policing strategies did not have a significant impact on the crime rate and disproportionately affected black and Latino populations (Badger, 2020). Increased police presence, however, did have a 5-6% reduction in the crime rate from 1991-2001 (Levitt, 2004). If all factors were incorporated into the statistical analysis, the reasons behind the dropping crime rate would have been more widely understood and certain actions could have been implemented across the country to reduce crime further.

References

Badger, E. (2020, November 30). The lasting effects of stop-and-frisk in Bloomberg’s New York. The New York Times. https://www.nytimes.com/2020/03/02/upshot/stop-and-frisk-bloomberg.html

Levitt, S. D. (2004). Understanding why crime fell in the 1990s: Four factors that explain the decline and six that do not. The Journal of Economic Perspectives, 18(1), 163–190.

Makin, T. R. & Xivry, J. O. (2019, October 9). Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript. eLife. https://elifesciences.org/articles/48175