Using Median to Quantify the Average Sales per Store
I will use the median measure of central tendency to quantify sales averages per store in the grocery business. Generally, the median hardly requires an overall summation of the sales values and figures (). Interests in the sales fraternity are to offer products that best suit clients to maximize sales. As a result, the median figure will give a good ground. Median gives a more representative analysis when understanding the whole scenario of the concerning factors (). Mean can easily generate surprising figures making things extensively generalized figures hard to apply in forecasting comprehensively. Therefore, the median offers a more reliable estimation for a central tendency.
Other basic concerns about the entire sales narrative are constantly making it convincing to resort to median use. On one side, the consistency of emerging distortion in the sales figures is a critical factor (). Working around reality is hard to undereat in the entire process of integration. Moreover, fidelity with relevance to sales factors is an important virtue I hold in high esteem. Consequently, it helps to answer the disturbing question of authenticity and reliability in the numbers coming up (). I think that focusing much on the mean demeans the process of working with reality in the domain of exchange.
In a nutshell, I am trying to say that analysis must be representative enough for it to save the course of sales forecasting. Handling numbers that are far from reality might make someone fail miserably in the entire process of sales narration (). It is only reasonable that no person would expect the business to go down on the scales of imagination and unreliability. Therefore, a more representative sales analysis is the best thing to do for a thriving corporate enterprise.
References
Šindelář, J. (2019). Sales forecasting in financial distribution: a comparison of quantitative forecasting methods. Journal of Financial Services Marketing, 24(3), 69-80.
Noyes, J., Booth, A., Moore, G., Flemming, K., Tunçalp, Ö., & Shakibazadeh, E. (2019). Synthesizing quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, and designs and outlining some methods. BMJ global health, 4(Suppl 1), e000893.
Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European journal of operational research, 290(1), 99-115.
Mester, T. (2018). Statistical Averages – Mean, Mode and Median – Data36. Data36. Retrieved 4 June 2022, from https://data36.com/statistical-averages-mean-median-mode/.