Biostatistics - Hypothesis and Inference Testing


To answer proposed research questions, it is essential to test the hypotheses in order to reach conclusions. In this session students will be introduced to the process of hypothesis testing, the meaning of type I and type II errors, as well as the interpretation of p values and confidence intervals.


  • Statistical inference: The process of drawing conclusions about a population from quantitative or qualitative information obtained from a sample of observations using methods of statistics to describe the data and to test suitable hypothesis.
  • Hypothesis: Is a prediction about what the examination of appropriately collected data will show.
  • Hypothesis testing: An approach to statistical inference resulting in a decision to reject or not to reject the null hypothesis.
  • Null hypothesis: The hypothesis that there is no real difference between groups (means, proportion, …etc.) being compared.
  • Alternative hypothesis: The hypothesis that a real difference exists between groups (means, proportion,…etc.) being compared.
  • Type I error: Rejecting the null hypothesis when it is true.
  • Type II error: Accepting the null hypothesis when it is false.
  • Power: Is the probability of detecting a differences if it actually exist.
  • Alpha level: The highest acceptable risk of committing type I error .
  • P value: The probability of observing statistic as extreme or more extreme than the observed statistic given that the null hypothesis is true.
  • Confidence interval: Range of values that describe uncertainty about an estimate.

Additional (Optional) Reading

Chapter 10: Statistical Inference and Hypothesis Testing