As someone deeply immersed in finance and accounting research, I often encounter situations where probability sampling—the gold standard for statistical inference—is impractical. Budget constraints, time limitations, or inaccessible populations force researchers to rely on non-probability sampling. While it lacks the rigor of random selection, non-probability sampling offers flexibility and cost-efficiency, making it indispensable in many

Data analysis forms the backbone of decision-making in finance, accounting, and many other fields. While much attention goes into minimizing sampling errors, non-sampling errors often lurk in the shadows, distorting results without warning. I’ve seen projects derailed because analysts overlooked these hidden pitfalls. In this guide, I’ll break down non-sampling errors—what they are, why they