Benford’s Law (BL) is being used extensively in research for several purposes including for the detection of potential manipulations of the data to detect fraud since datasets tend to follow the Benford’s distribution when they occur naturally without artificial control. The COVID-19 pandemic has heavily impacted business and non-business-related activities. Datasets related to the pandemic are being used in many different analyses to arrive at different conclusions. However, the credibility of the results and conclusions depend heavily on the accuracy of the datasets. The COVID-19 related datasets are obvious results of intense human intervention and artificial control efforts; therefore, the question arises as to whether Benford’s analysis can still be used to detect anomalous datasets among them? This research uses several publicly available datasets and uses predictive analytics to perform the Benford’s analysis. The applicability of BL is first verified using a regular dataset occurred prior to the pandemic, and then applied on COVID-19 related datasets to test the research hypothesis.  The results demonstrate that even the datasets with sufficiently large sample sizes with considerable human intervention and artificial control follow the Benford’s distribution and that Benford’s analysis can still detect the  anomalous datasets. The findings are anticipated to be useful for the data analysts and researchers and adds to the current literature gap. This paper may also serve as a class case study for the academia teaching data analytics.