Understanding Statistical Inference in Medical Statistics

After learning how to define the features of a data set in Medical Statistics I, the next step is to understand how to draw conclusions about a population based on a representative sample through statistical inference. This process involves expressing the probability of an event occurring rather than declaring it as a fact, which helps in addressing uncertainties and generalizing findings.

Real-Life Applications of Statistical Inference

Statistical inference can be used to address various questions in medical statistics, such as:

• What is contributing to higher rates of COVID-19 in millennials than Gen Z?
• Why are some ethnic groups predisposed to childhood obesity?
• What factors are contributing to a higher prevalence of cancer in some regions of the country?

Hands-On Computer Simulations

By using hands-on computer simulations in R or SAS, you can apply statistical inference to make predictions about data. This practical approach helps in understanding the foundations of probability, including:

• Basic probability
• Probability distributions
• Standard error
• Confidence intervals
• P-values
• Statistical power
• Type I and Type II error
• Bayesian statistics
• Equivalence testing

Avoiding Common Statistical Misconceptions

It’s essential to recognize common statistical misconceptions and errors, which can often be found in real-life examples from medical journals and the popular press. By understanding these pitfalls, you can ensure that your statistical analysis is accurate and reliable.