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Sometimes we use probability distributions that are not exact representations of the physical processes that they are meant to represent. (For example, we might use a normal distribution for a distribution of individuals’ weights, even though no one can weigh less than zero pounds.) Why do we do this? (Please include at least 1 reference and in text citation)
A probability distribution and the process of calculating the chances of an outcome depends highly on the available input. The more accurate the input is, the better precise the output gets. In real life situations though, managers who are in dire need of good decisions, do not have all the required information in hand, that is they can only predict how scenarios will unfold in the market, and use these data as input in calculating the probability distribution. Sometimes, the near perfect probability distributions can help in achieving the desired effect or output when reliable data is not available (Yan, Xiang, Li, Hu, Zhou, Wang, & Meng, 2022). Probability computation works well when at least some conditions are met, then long term average outcomes are calculated the results are derived by estimating all the variabilities that are possible through random variables, where there are high chances of detecting the actual outcomes of these random probabilities.
When a scenario is presented to the management, an in-depth analysis of its probability distribution can provide valuable insights to the decision makers of worst-case scenarios and best-case scenarios (Chang, Lee, Lee, & Lu, 2022). When these distributions are not exact due to changing variable or unforeseen change in variables like market conditions, sales, technological innovations, then the calculations fall into the umbrella of desired output though not exact; and this process can be an opportunity for the management to plan a course correction plan or even a backup if things do not go as planned. Therefore, the leadership teams can draw up a business plan that aligns with the most likely scenarios, yet at the same time be aware that there can be alternate possibilities too due to the input variations.
Yan, P., Xiang, C., Li, T., Hu, X., Zhou, W., Wang, L., & Meng, L. (2022). Research on Probability Distribution of Short-Term Photovoltaic Output Forecast Error Based on Numerical Characteristic Clustering. Computational Intelligence & Neuroscience, 1–11. https://doi.org/10.1155/2022/5355286
Chang, T., Lee, S., Lee, J., & Lu, C. (2022). An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction. IEEE Access, Access, IEEE, 10, 6417–6429. https://doi.org/10.1109/ACCESS.2022.3142083