The inappropriate use of statistical methods and techniques cause time and money and can be misleading to other researchers, the most common statistical error sources are determined examining the previous medical researches and taking errors into account occurred in researches during statistical consulting (Iiker Ercan). There are several errors that researchers easily commit if they lack solid statistical background. The mistake in the studies mostly occur because of the researchers lack statistical knowledge and since they don’t take statistical consulting, unbiased, consistent and efficient parameter estimates are made in statistics.
In my own opinion, from what I have learn so far in this course, there are lots of inappropriate use of statistics in our every day life. I read in an article in the “preventive guide” discussing a topic “Walk off Weight and bye bye belly fat, walk a little and Lose a lot”, This article discussed how you will walk and, in few weeks, you lose 5 inches in just weeks. This article did not go into detail to discuss other things that the participant was doing like the feeding and other exercises that might be involved to achieve the desired goal. Anybody looking at this statement will believe that you just walk and lose that weight and uses the program and when it does not happen as stated the person will be disappointed.
Look also at people in cooking commercials, in 1 minute they started cooking and the next minute the food is done and ready to serve, that is inappropriate presentation of statistics. comment 2.
After having basic knowledge of statistical terminology in clinical research some ways that I can recognize inappropriate use of statistics can lead to false conclusions. For example, misuse of p values for significance testing can lead to an increase in errors or analyzing data inappropriately can also cause result in reporting bias. According to Charan and Saxena ( 2015) one of the most mistakes during clinical trial research is the misuse of statistical test, or inappropriate tests. In this case the data distribution can be skewed if sample sizes are too small. Charan and Saxena (2015) also discuss the use of wrong statistical testing affecting the results or conclusions in clinical trial research.