Statistics and Real Life

Statistics and Real Life

Statistical knowledge has had profound impact in both personal and professional life. For example, in my personal cum professional life since I run a fast food restaurant, I have used measures of central tendencies such as mean, median and mode to gauge the nature of the market. For example, I have used the mean calculated over a month to determine the average number of scones I must have in stock daily using the customer’s data for scones (Falorsi, Righi, & Lavallée, 2019). This has informed my production decisions since it eliminates wastage and misuse of resources. Further, I have used mode to determine the beverage that sell most in the restaurant and its flavors by the patrons who visit the restaurant. The mode has supported my stocking decisions. The median has helped me to determine the walk-in customers who visit the restaurant to buy both soft drinks and snacks to take on board rather than take away. The median value has informed layout planning at different times of the day.

Due to the emergence of computerized software for data analysis, organizations are depending on generated data for decision making than before. Moreover, businesses are engaging firms to conduct research on the pain points of their customers and the trends of competitors to enable them retain their market. The analyzed data once it reaches the client, they use it in initiating turn around strategies that makes them beat the competition in the operational environment. Statistics can be persuasive or misleading depending on the size of sample, the sampling techniques and data analysis system used (Singh, & Masuku, 2014). A larger sample is more representative of the population making it more persuasive due to reduced error of margin rather than a small sample that can be misleading with same confidence say of interval of 95%. The sampling technique will be better as well as more persuasive if all members of the population stands equal chances of being selected hence not misleading.

References

Falorsi, P. D., Righi, P., & Lavallée, P. (2019). Optimal Sampling for the Integrated Observation of Different Populations. Survey methodology, 45(3), 485-511.

Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of economics, commerce, and management, 2(11), 1-22.

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