Charlotte Pritchard's 'Does Chocolate Make You Clever?'

Analysis of Statistics in Everyday Media In the article, "Does Chocolate Make You Clever?" by Charlotte Pritchard, the explanation of a study by Franz Messerli was provided. Messerli wanted to investigate how chocolate can affect your intelligence after reading that cocoa can lead to improved mental function in elderly patients with mild cognitive impairment and to a longer life with improved memory. In his study, Messerli took the number of Nobel Prize winners in a country as an indicator of general national intelligence and compared that with the nation's chocolate consumption. The r value, or correlation coefficient and P value was calculated using a scatterplot of the data. The correlation coefficient (r value) is a quantitative assessment of the strength of relationship between the x and y values in a set of (x, y) pairs. The value of r is a measure of the extent to which x and y are linearly related or the extent to which the points in the scatterplot fall close to a straight line. The value of r is between -1 and +1. A value near the upper limit, +1, indicates a substantial positive relationship, whereas an r value close to the lower limit, -1,
suggests a substantial negative relationship. The calculated r value indicated by the data in the article was 0.791. This value suggested a moderately strong, positive, linear association between chocolate consumption and number of Nobel prize laureates per capita. A "P" value of approximately 0.0001 was also calculated.
The p-value or "statistical significance" of a result is the probability that the observed relationship between variables in a sample occurred by pure chance. As stated in the article, this means there is a less than one-in-10,000 probability of getting these same results if no correlation existed. Another statistical concept in the study was the use of a scatterplot to represent the data. A scatterplot is a summary of a set of bivariate data (two variables), usually created before calculating the linear correlation coefficient or fitting a regression line. The use of the scatterplot in this research study was to give a better visual picture of the relationship between the two variables and aid in the interpretation of the correlation
coefficient. An outlier in the data would be Sweden because despite the very high number of Nobel laureates, the people living there consumed much less chocolate on average. These statistical concepts and the outlier lead to imply that the two variables in this study correlate closely, but there are no reasons to indicate any causation that can be concluded as well. The data forms a case where a strong correlation does not mean causation.