Many experiments still rely on outdated techniques or standards that fail to meet up to the level of detail and accuracy required by the public and researchers of this era. In that, experiments that aren’t modernly designed might reach conclusions that have neglected important details regarding the subject matter. Specifically, results could lend to generalizations about a certain phyla of bacteria, wherein the multitude of strains it encompasses actually does not meet conclusions made by the researchers.
Does the study show causation or correlation?
Though the two relational classifications are widely acknowledged as important to distinguish between, it can often be a difficult task. Categorizing two variables as having a causal link when they are merely just victims to same-place-at-the-same-time circumstances would be a significant mislabelling that could inspire additional unnecessary research.
What is the mechanism?
It is important to define the process by which the science in question actually works. To outline and understand the cause and effect being published in the study/research will eliminate some mystery as to if the two variables are actually related in a causal way. Determining each step of the mechanism from beginning to end will validate the conclusions reached by the study, rather than simply stating two things are related due to their mutual presence.
How much do experiments reflect reality?
While significant and groundbreaking discoveries can be made during research projects/studies, it is not always clear if the given results/findings hold the same significance when applied to human life. For example, most experiments use specific animals to reach their conclusions, but it must be taken into account that this does not efficiently mirror an experiment carried out on humans. It must be acknowledged in the work and the differences should be explained to better keep the data in perspective.
Could anything else explain the results?
Similar to the question of cause or correlation, an audience should ask if the circumstances hold any variables that could be blamed for the ending data. If there are other influential things present (e.g., environmental, etc.) then it is not a fair assessment or true discovery.
