One of the mistakes people make is assuming that data are infallible. When some individuals want to prove the accuracy of their assertions, they back them up with related data and expect everyone else to accept their argument. Of course, it is always good to use facts and figures as a buffer for arguments but that doesn’t mean that what is said is true. The truth is, “data” is not always right. This is one thing you should always bear in mind each time you pull up published information or data as the basis for your argument.
You see, what is written on paper or published on the internet is not the problem but the processes it passed through before it was published. If you had noticed, while you were in school (or even right now in your office), you are always expected to use different sources for writing an assignment, a proposal, a report, or a project. The people that tell you to do so are not out to stress you but to make sure that the imperfection in the data you are using is reduced. If you pay attention to the differences in the data published about a particular phenomenon, you will discover the discrepancies in them. Of course, ordinarily, factors, such differences in age, gender, class, race, religion, cultural orientation, financial status, social environment, and so on, affect data. But data collected from the same group of persons within the same time frame and by the same persons (or maybe different persons) can come out differently. This is why you shouldn’t always “swear” that what is published is the absolute truth because you think it is objective.
As mentioned earlier, sometimes, the same set of data can be collected from a group of respondents but they will not give the same result. The factors that can cause these discrepancies can come from the data collection method, the data collector, and the respondents themselves.
The method used in collecting data affects what is gathered. For instance, a questionnaire is supposed to be the most unbiased method but then, it makes respondents self-conscious and less willing to reveal sensitive information. People easily lie on questionnaires. Then, the interview is another avenue that allows respondents to be studied objectively. But it is not all information that can be gathered through interviews. Besides, respondents can also lie through this method. Finally, the observation method is usually the best when the data collector wants to get unbiased data from respondents. However, this method works better when the observation is carried out over a long period and the data collector is not biased.
Data collectors also contribute towards making data fallible. People sit down in their houses to falsify information based on “what they see happening around them.” They assume for the respondents and, so, supply data based on what they think and not on what is real. Some collectors also manipulate respondents into supplying the type of information that suits their (that is collectors’) narratives. There are also cases of misrepresentations, where data collectors select respondents that are either unsuitable for the research or those that will supply the desired information. All these and more are reasons you should be careful with how you rely on published data.
There are so many factors that can make respondents supply different information at different times. The first one is emotion and other physical conditions, such as illness, hunger, and the rest of them. Then, there are respondents that deliberately supply inaccurate information or even refuse to supply any. Some misunderstand what was demanded from them and many provide information to impress the data collector.
You know, the reasons mentioned above are part of why the use of secondary data is not always encouraged in any research project. Unless you are the one that collected the data, you cannot boldly use them to arrive at a conclusion. This is just to say that you should take all these figures you see out there with a pinch of salt because not all of them are facts.