What Makes Scientific Research Good?

Many researches today are labeled and promoted as “scientific” though they lack of scientific basic. A scientific research is an evolving process. Therefore, there are certain widely accepted requirements for referring a research as scientific. Here, fundamental characteristics of scientific research is shown.


The research should have a definite aim and purpose

The research should have a definite aim and purpose focus. A researcher should concentrate on solving a well‐identified and defined problem, rather than looking for answers to vague, aimless questions.

For instance, a manager would aim to increase employee commitment to the organization. A university dean would like to know why students in business administration have higher level of absenteeism compared to other students.


A rigorous research should have a good theoretical base and a carefully designed methodology. Researchers can collect the right kind of information from an proper sample by considering possible bias.

The data gathered should lead to suitable analysis. Studies with rigor are conducted in a meticulous, careful and flawlessly.


After a careful study, researchers should be able to test logically developed hypotheses. Hence they can see whether or not the data support the educated conjectures or hypotheses.


A good scientific study should also allow us to replicate the studies in other settings. Our studies would be much more credible and trustworthy if similar findings emerge on the basis of data collected by others conducting the same methods.

Indeed, with replication of the study proves that supported findings are not just pure luck. As expected, when the same type of research is repeated in similar circumstances, those who conduct the studies would have more confidence in the scientific nature of the research.

Precision and confidence

Precision and confidence

In many scientific researches, %100 percent accurate results may not always be possible. The sample in question may not reflect the exact characteristics of the phenomenon. In addition to these, bias situations, measurement errors can affect the results. However, we would like to conduct the research in a manner that ensures that our findings are as close to reality as possible. Hence, we can have reliance or confidence in the results.

Precision shows how accurate and exact the study results on the basis of the sample. For example, let assume that you have a toy factory. You try to figure out production lost due to manufacturing defects. Your estimation between 3000 and 4000 unit faulty products against as against the actual figure of 3500 would be more favorable than estimation between between 2000 and 5000.

Confidence also called as the confidence level. It refers to the probability that our estimations are correct. Besides being precise, you should also confidently claim that 97% of the time our results will be true and there is only a 3% chance of our being wrong.


Objectivity in scientific researches

Researchers should be objective and take findings derived from actual data into account. The interpretation of the results on subjective terms would cast a shadow on objectivity.


Scientific studies also provide generalization of its findings for other settings. Generalizability has paramount importance for wider range of users who utilize solutions generated by research.

For instance, the findings that decreased employee commitment leads to higher level of absenteeism would be valuable if found to be true in a variety of manufacturing, industrial, and service firms, and not just in the particular organization.

However, it should be noted that wider generalizability with bigger sampling will increase the costs. Therefore, especially most applied studies restricted applicability, which does not necessarily decrease its scientific value.


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Researchers should explain the problem. They should provide its solutions with simple and understandable manner. Therefore, complex and cumbersome research frameworks should not be preferred.

A research framework with lesser variables can explain far more efficiently than a complex one. In other words, complex set of variables with marginal add are not needed.

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