Statistics is arguably one of the most critical fields that can assist researchers in collecting, analyzing, interpreting, and present data in a manner that the intended audience can easily understand. Arguably, from sociology to medicine, statistics is a critical process that assists people to make scientific discoveries, make predictions and assumptions, as well as make decisions based on the information presented. My performance in this class has, without a doubt, contributed substantially to my ability to read and interpret various biostatistical data.
For example, lessons learned in this class has boosted my ability to describe biostatistical data using graphs and other descriptive methods, including bar graphs, line graphs, pie charts, and many others. I am confident that I can now predict cancer trends by calculating the mean, mode, variance, standard deviation, range, and median when presented with oncology data. I can use mean, for instance, of breast cancer patients to estimate the exact number of women presently suffering from the disease. Besides descriptive statistics, I believe lessons learned in inferential statistics has equipped me with crucial knowledge in making deductions and inferences using biostatistical data. I can use t-tests, ANOVA, linear regression, correlations, and many other inferential methods to determine if they are any statistical correlation between data. Other lessons I believe have improved my statistical analysis skills include probability concepts and use of Excel to perform various descriptive and inferential procedures.
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Lastly, I believe I can build on this knowledge by learning how to use particular statistics software such as SPSS, a product by IBM that allows for batched or interactive analysis. I can apply this statistical knowledge in oncology informatics, which will enable me to use health IT to better the processes of diagnosis, management, and treatment of cancer patients. I believe inferential methods like t-tests and descriptive techniques such as mean can assist in predicting future trends in cancer treatment and other processes.
Response to Student 1: (Ellen)
I agree with Ellen that statistics is one of the core parts of quantitative research and statistics. Besides, I accept that Excel is one of the few powerful tools that can aid in understanding statistical data, including making presentations and inferences. Excel can perform critical functions like determining probabilities, creating frequency tables, testing hypothesis, carrying out regression analysis, and many others. I also concur that software like Tableau and SPSS make work even more straightforward.
Response to Student 2: (Jerry)
I agree with Jerry that statistics is an essential subject in biostatistical information studies. Besides methods like T-test, regression, chit test, and ANOVA assisting in interpreting data, they also help learners learn how to apply them in real life situations. Just like Jerry notes, these methods involve lots of calculations and take time to grasp; therefore, practice and persistence is needed to understand them completely.
