This paper presents an operational metric for univariate unimodal probability distributions with finite first moments in [0,1], where 0 is maximally thin-tailed (Gaussian) and 1 is maximally fat-tailed. It is based on the question, “how much data does one need to make meaningful statements about a given dataset?”Applications: Among others, it •helps to determine the sample size n needed for statistical significance outside the Gaussian distribution,•helps to measure the speed of convergence to the Gaussian (or stable basin),•allows practical comparisons across classes of fat-tailed distributions,•allows the number of securities needed in portfolio construction to achieve a certain level of risk-reduction from diversification to be assessed,•helps in assessing risks under various settings, and•helps to explain some inconsistent attributes of the lognormal, depending on the parametrization of its variance.The literature in regard to asymptotic behavior is rich, but there is a large void regarding finite values of n, which are those that are needed for operational purposes.Background: Conventional measures of fat-tailedness, namely (1) the tail index for the power law class, and (2) the kurtosis for finite moment distributions, fail to apply to some distributions, and do not allow comparisons across classes and parametrization; that is, between power laws outside the Levy-stable basin, or power laws and distributions in other classes, or power laws for different numbers of summands. How can one compare a sum of 100 Student t distributed random variables with three degrees of freedom to one in a Levy-stable or a Lognormal class? How can one compare a sum of 100 Student t with three degrees of freedom to a single Student t with two degrees of freedom?We propose an operational and heuristic metric that allows us to compare n-summed independent variables under all distributions with finite first moments. The method is based on the rate of convergence of the law of large numbers for finite sums, specifically n-summands.We get either explicit expressions or simulation results and bounds for the lognormal, exponential, Pareto, and Student t distributions in their various calibrations, in addition to the general Pearson classes.