An Introduction to Biostatistics
In this chapter, we discuss the basics of what you need to know about biostatistics in order to statistically analyze and interpret the data from your in vitro and preclinical in vivo experiments. Experiments are conducted to answer one or more specific scientific questions, and they must be designed so that they are likely to provide answers with minimal bias and appropriate measures of variability and significance. Here, we discuss different methods of analysis and their accompanying assumptions. In addition, we cover several different experimental design considerations as well as the subsequent interpretation and graphical presentation of data and statistical findings. Furthermore, we provide insight on both sides of the debates surrounding controversial issues such as testing multiple hypotheses in a single study and addressing outliers in the data. We conclude with a discussion of the future of biostatistics for in vitro and preclinical experiments, highlighting the importance of learning biostatistical software in your training. We suggest you read this chapter before you begin performing experiments and collecting data.
KeywordsBiostatistics P value Summary measures Statistical power False positive rate Confidence interval Sample size Outliers Multiple comparisons
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