Limitations of traditional data analysis?



It is important to take into account the limits of traditional data analysis. Its heavy reliance on presumptions regarding the data and the underlying distribution is one of the downsides. The analysis's results and conclusions could be incorrect or erroneous if specific assumptions are broken. Another downside is that it often focuses on analysing data as a whole, which could leave out major differences or changes at the individual level. 

It could be unable to represent the nuance and complexity found in the data.

 Furthermore, independence between observations is often assumed in traditional data analysis, which may not be true in many real-world situations. Biassed interpretations and erroneous outcomes may emerge from this. Large and complicated datasets may also be difficult for standard data analysis to handle since they may require more resources and processing capacity than.

As a result, it is important to recognize these limitations and consider alternative approaches, such as machine learning or data mining techniques, to address these challenges in data analysis.


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