There are a vast universe of statistical tools used in medical research. How to make sense out of the data (interpretation of results) What to do with the data (choice of estimation test) Understand the origin of the data, by way of the research objectives
The process can be broken down into 3 clear steps:
However, since data in itself is never normally distributed or perfect, it becomes important to apportion a percentage of 0.01 (1%) as the level of significance or margin of error or probability that the result will produce an error, though slight but gets better as the benchmark approaches 100%, in other words, 0.001 (0.1%) or 0.0001 (0.01%).ĭata in itself consisting of an admixture of numeric, string and alpha numerical points can appear intimidating but the analysis of data does not have to be always complex. In testing research hypothesis, the assumption is based on 100% correctness. Validation parameters must be more stringent. In the medical research field, stretching from systematic reviews, meta-analysis and clinical trials, exactness and precision is paramount. Today, due to the gradual advancement in technology, statistical tools are being used in medical research for greater efficiency and accuracy. This opened up gaps for human error and greater cost of carrying out research especially when the data was large by say, over a 1000 field observations. There was a time when validating experiments through data was done fully using manual computation. Kolabtree freelance statistician Kingsley Ukwuoma writes about the top statistical tools used in medical research and clinical data analysis.
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