Scientific bias and other shenanigans

Bias is a very common problem in scientific research.  I was surprised to find that there are so many types of bias that may occur in any given study. It is important to identify bias at all levels of a study and these levels include: pre-study stage, study stage and data analysis stage. The overall problem with bias is that results can become skewed and create results that are not correct. One interesting aspect of bias at these different stages is that they can be limited through careful planning and awareness. For example, selection bias is a very common form of bias that occurs during the pre-study stage. In this form of bias a study may become compromised due to recruitment strategies for selecting study participants that rely on criteria that favor one group over another. This may lead to decreased probability of identify a difference between groups when one is present. One way of combating this bias is to use random selection. By randomly assigning individuals to groups in the study the chance of having a selection bias for one group over the other is greatly reduced. 

One aspect of Bias that is important to understand is the difference behind intentional and un-intentional bias. Essentially bias is never completely accounted for in a study. No study is perfect. But what is interesting is when the bias is known to exist or even created by by the researchers but they do not correct for it in their study. This is what I refer to as intentional bias also known as academic misconduct. I think the primary reason for not making these essential corrections are largely due to the pressures put on researchers to publish. Without the papers it is hard to get funding. It is not hard to believe that someone would be tempted to publish tampered results if it means they will be able to support themselves financially. Another reason could be the fame. An example of fame seeking would be the story of Hwang Woo-suk, a stem cell researcher who in 2005 was found to have fabricating a large number of experiments leading to papers published in top-tier journals. This was a heavily reported incident. In this case, it is shown that bias can have a negative effect on public opinion of scientific research. This opinion is important for continued growth and support of scientific research.