SIR-IN creates medical research continuing education based on current Information and Communication Technologies. By entering into our scenes you will feel yourself as being one of our characters trying to solve research common problems. Our teaching team has broad experience on training health professionals on research methodology, and is already very familiar with the most common mistakes and questions among professionals. The wright solutions will be provided as pearls that will be applicable immediately. Here you have some examples of our new teaching method.
What would be the result of a wrong sample size?
One of the most important aspects when conducting research, is to determine the required sample of subjects. Thus, a low number of subjects would lead to a lack of statistical power so that the relationships under investigation could be observed. This can also result in the well-known type II error, concluding that there are no differences between the factors and studied outcomes. However, this fact cannot be solved with a massive inclusion of subjects, because in this case it could happen that results were statistically significant but they were not clinically significant.
What would be the result of not having determined the confounding variables?
When conducting research, researcher have to be aware of that there are other factors to take into account that are different from the variables and outcomes data they are collecting. In fact, there are other type of variables that need to be controlled in a research study. These are the factors that could confound the results, since they are related both to the dependent and independent variable. However, they have no causal relationship with the scientific fact under study. These confusion variables are not identical among investigations, and have to be previously determined for the data-collecting period and the statistical analysis.
What would be the result of having selection bias because of a wrong sampling technique?
The ecological validity of a research is referred to the degree of generalization of the results in the sample under study to the reference population. It is especially important to select a representative sample of the subjects’ population with the characteristic under study. In cross-sectional studies, it is very common to use probabilistic sampling techniques, so that every single subject has the same probability of being selected for the research. Also, in analytical observational studies, a core issue is how to properly determine the comparison control groups. If this was not adequately performed, then a selection bias could invalidate the results.
What would be the result of not choosing the proper biostatistic estimator?
Since medical research seeks to generalize some results in a particular sample of subjects to a reference population, biostatistics is an essential tool. We must make sure that the results are not incorrect and that the random components have been controlled and quantified in the analysis. The statistics will only be able to generalize evidence under some assumptions such as the distribution and randomness of the data. In other words, given the distribution and randomness of the data, using the most appropriate statistical estimator is crucial. Otherwise, the research results will be very different from the reference population status.
