Limitations of traditional data analysis


 7.    What are the limitations of traditional data analysis?



Traditional statistics is about analysing and summarizing data, and suited to processing lesser amounts of linear, repeatable data to find a solution. It’s very useful in environments where data, and the relationships between the data, are relatively stable. It is still by far the most commonly used methodology and continues to be good for mid to long term forecasting based on prior sales history, where a few hundred or even fewer data points may generate a reasonable forecast.


The important limitations of statistics are:

 

  • Statistics laws are true on average. Statistics are aggregates of facts, so a single observation is not a statistic. Statistics deal with groups and aggregates only.
  • Statistical methods are best applicable to quantitative data.
  • Statistics cannot be applied to heterogeneous data.
  • If sufficient care is not exercised in collecting, analysing and interpreting the data, statistical results might be misleading.
  • Only a person who has an expert knowledge of statistics can handle statistical data efficiently.
  • Some errors are possible in statistical decisions. In particular, inferential statistics involves certain errors. We do not know whether an error has been committed or not.
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References:
https://floyden.home.blog/2019/04/22/limitations-of-traditional-data-analysis/

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