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Statistics- interpretations or misinterpretations?

The statistics of market research play an important role in deciding the future course for the company. Statistics, although mostly accurate, can go awry if the data is not collected properly if a certain amount of data is misplaced, etc.
Market researchers must look carefully at data regarding the size of the sample, questions asked, and the leanings of each question. It is important to ask why the research was done and what the results say.

Despite well-done market research, statistics can go wrong, if:

If the sample is too small

Although a smaller sample is convenient for research purposes since the collection and interpretation will take lesser time. But the results of a small sample cannot be a reflection of an entire population. Moreover, it could also confine the sample to people of a certain taste, geographical location, etc. Market research must be able to justify the sample size to determine the reliability of results.

If Statistical Error Margins Are Too Large

Error margins are a way of knowing how accurate the statistics are. When the sample size is too small, error margins are large. When error margins are large, the chances of error are too. So, it is important to maintain smaller margins for statistical accuracy.

If The Sample Representation Is Inaccurate or Biased

While collecting a sample, the sample must match the kind of audience that the business is targeting, or else the data interpreted will not be accurate or applicable to that particular business.
Similarly, if people have a misunderstanding with regards to the research, they may give responses that mislead the research.

Incentives are Inappropriate for the Statistics Sample

If an incentive is given to the respondents, chances are they will respond in a biased manner that will lead to inaccurate results.

The Research and Statistics Context Is Not Reported

A lot of times statistics are quoted without context. A statistical interpretation done without understanding the context is inaccurate because the values may apply to a certain situation but not to another.

The Statistic Flies in the Face of Precedent

Statistical data that goes against the usually accepted is to be approached with caution. It may not be wrong but it is always better to double-check before believing it.

Statistical misinterpretations can be avoided by fixing pre-statistical problems. When it comes to statistics even the smallest errors can lead to misinterpretations. It’s always better to prevent than to cure.

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