why do such important misconceptions about research persist ? to a large extent these misconceptions represent substitutes for more thoughtful and difficult tasks . it is simpler to resolve a discrepancy between a trial and a nonexperimental study in favor of the trial , without undertaking the laborious analysis that hernan et al . did.10 it is easy to declare that a result is not statistically significant , falsely implying that there is no indication of an association , rather than to consider quantitatively the range of associations that the data actually support . these misconceptions involve taking the low road , but when that road is crowded with others taking the same path , there may be little reason to question the route . indeed , these misconceptions are often perpetuated in journals , classrooms and textbooks . i believe that the best prospect for improvement is to raise consciousness about the issues , with reasoned debate . max planck once said ,  a new scientific truth does not triumph by convincing its opponents and making them see the light , but rather because its opponents eventually die , and a new generation grows up that is familiar with it.31 to the extent that this cynical view is correct , we can expect to see outmoded concepts fade away slowly at best . i hope that calling attention to these misconceptions will spark the needed debates and be a catalyst for change .
abstractscientific knowledge changes rapidly , but the concepts and methods of the conduct of research change more slowly . to stimulate discussion of outmoded thinking regarding the conduct of research , i list six misconceptions about research that persist long after their flaws have become apparent . 
 the misconceptions are : 1 ) there is a hierarchy of study designs ; randomized trials provide the greatest validity , followed by cohort studies , with case  control studies being least reliable . 
 2 ) an essential element for valid generalization is that the study subjects constitute a representative sample of a target population . 
 3 ) if a term that denotes the product of two factors in a regression model is not statistically significant , then there is no biologic interaction between those factors . 
 4 ) when categorizing a continuous variable , a reasonable scheme for choosing category cut - points is to use percentile - defined boundaries , such as quartiles or quintiles of the distribution . 
 5 ) one should always report p values or confidence intervals that have been adjusted for multiple comparisons . 
 6 ) significance testing is useful and important for the interpretation of data . 
 these misconceptions have been perpetuated in journals , classrooms and textbooks . 
 they persist because they represent intellectual shortcuts that avoid more thoughtful approaches to research problems . 
 i hope that calling attention to these misconceptions will spark the debates needed to shelve these outmoded ideas for good .