Advanced Method for More Accurate Estimation of Normal Distribution Mean

Article ## An Improved Method for Estimating the Mean of a Normal Distribution

Abstract:

The objective of this paper is to enhance an existing method for estimating the mean value of a normally distributed population. The standard procedure involves calculating the sample mean, but recent studies have highlighted several limitations and biases associated with this approach.

By conducting extensive simulations across various scenarios, we develop and refine new estimation methods that are more robust and less sensitive to outliers than traditional estimators. We introduce an innovative technique that combines multiple estimates of the population's central tency under different assumptions about its distributional characteristics.

These enhanced estimators significantly reduce bias in both small and large sample sizes compared with conventional techniques, such as the simple arithmetic mean. Furthermore, they perform excellently when dealing with datasets that have extreme observations or heavy-tled distributions.

The paper also presents a comparison between our new method and existing ones based on their accuracy, efficiency, robustness, and computational complexity. Results demonstrate that our approach consistently yields more accurate estimates of population means across diverse conditions while being computationally efficient.

In , we provide an improved technique for estimating normal distribution mean which is not only more precise than traditional methods but also handles complex data sets better. This improvement offers a valuable tool for researchers who often need to make inferences about populations based on sample statistics.

Keywords: Estimation; Mean of Normal Distribution; Bias Reduction; Robustness; Simulation Studies
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