Final answer:
The two schools of thought within biostatistics are Frequentist and Bayesian, with distinct approaches to probability and inference. The Frequentist method is based on long-term frequency, while Bayesian incorporates prior knowledge. Statisticians use various data types and sampling methods to make appropriate inferences about populations.
Step-by-step explanation:
The two schools of thought within biostatistics are Frequentist and Bayesian. These two perspectives differ primarily in how they interpret probability and how they address the process of inference. The Frequentist approach is the more traditional form of statistics, focusing on long-term frequencies of events through repeated sampling. Frequentists make inferences from data by using methods that control for frequencies of errors over many hypothetical repetitions of the same experiment. On the other hand, the Bayesian approach incorporates prior knowledge or beliefs in addition to the data at hand, updating the probability estimates as new data becomes available. Bayesians use probability more subjectively as a measure of belief or certainty in an event’s occurrence.
Descriptive statistics summarize data from a sample using measures such as the mean or standard deviation, while inferential statistics involve making predictions or inferences about a population based on a sample. During hypothesis testing, a statistician will use the collected data from a sample to make a decision about whether to reject the null hypothesis. A hypothesis test might involve comparing two means or two proportions, with variances that can be either known or unknown, which determines the distribution used to perform the test, such as the Student's t-distribution for matched samples or the normal distribution for two population proportions.
Statisticians must also consider the type of data they are analyzing, whether it be qualitative (categorical), quantitative discrete, or quantitative continuous, as each type of data requires different statistical models and methodologies. Moreover, appropriate sampling methods, such as simple random, systematic, stratified, or cluster, affect the accuracy of the inferences made about the population.