“It is never safe to take published statistics at their face value without knowing their meaning and limitations.” Elucidate this statement by enumerating and explaining the various points which you would consider before using any published data. Illustrate your answer by examples wherever possible.
Answers
Explanation:
land land land land land land
Answer:
"It is never safe to take published statistics at their face value without knowing their meaning and limitations.” means that published data is not completetly reliable and pose limitations.
Explanation:
Disadvantages/Limitations Of Published Data:
(1) Old and outdated nature: Chances of published data being old and outdated is huge and using such data for current scenarios may do more harm than good. As utility of secondary data reduces with time.
For example: Business environment is dynamic in nature and any published data used in such circumstances will not be relevant to use.
(2) May not be complete and reliable: Published data is secondary data and purpose of collecting such data may vary. Thus before using secondary data one should give proper attention to the completeness and reliability of the data. Moreover, use of unreliable published data is dangerous for research purpose.
For example: Suppose a businesses target audience is age group 2-15 years and we need data to understand their tastes and prefrences but the published data is available for age group 2-10years old thus there will be a doubt on completeness and reliability of such data.
(3) Too much dependence undesirable: Too much dependence on published data is not advisable as the purpose of collecting any data may vary, along with that the data being old and outdated could definitely be an issue.
For example: An organization being totally dependent on published data in this dynamic environment will be using ambiguous data.
(4) May be of bias nature: The chances of published data being biased in nature is real high as researchers collecting the data may forge the data for their own good.
For example: Agencies gathering data may try to forge the data to show a better picture of situations then the reality. Like ambiguity may be created to showcase the standard of living of citizens to get more votes in elections.