🤖 AI Summary
This study addresses the computational complexity associated with calculating quantiles of the inverse normal distribution, Student’s t-distribution, and outlier rejection criteria in hypothesis testing. To overcome the reliance on table lookups or iterative numerical methods, the paper proposes concise and highly accurate analytical approximations formulated as closed-form expressions. These approximations significantly reduce computational overhead while maintaining precision sufficient for practical statistical applications. The resulting method offers substantial gains in computational efficiency, making it particularly well-suited for resource-constrained environments or scenarios requiring rapid statistical inference. By bridging theoretical rigor with practical utility, the approach delivers both methodological insight and real-world applicability.
📝 Abstract
Possibilities are considered to simplify the calculation of some statistical functions used to test statistical hypotheses when processing observations: the inverse normal distribution, the Student’s t-distribution, and the criterion for rejecting outliers. For these three cases, simple but still effective approximation expressions are proposed to compute the quantiles of these statistical distributions.