The field of **statistics** is concerned with collecting, analyzing, interpreting, and presenting data.

**Biostatistics** is simply the application of statistical methods to topics in biology.

For example, a student who takes an introductory statistics course may learn about the following topics:

- How to calculate descriptive statistics
- How to visualize data
- How to construct confidence intervals
- How to perform hypothesis tests
- How to fit regression models
- How to fit ANOVA models

A student who then takes a biostatistics course would learn how to apply each of these statistical methods to answer research questions in biology, public health, and medicine.

If a student wants to become a biostatistician, they must first learn about the concepts taught in an introductory statistics course.

They can then take a biostatistics course to learn how to apply statistical methods to specific research questions in the field of biology.

**Common Statistical Methods Used in Biostatistics**

The field of biostatistics uses many statistical methods.

The following examples illustrate some methods that are commonly used.

**Example 1: Hypothesis Tests**

Biostatisticians frequently use hypothesis tests to determine if a new drug causes improved outcomes in patients.

For example, a biostatistician may administer a blood pressure drug to 30 patients for one month and then administer a second blood pressure drug to the same 30 patients for another month.

Then, they may perform a paired samples t-test to determine if there is a statistically significant difference in blood pressure reduction between the two drugs.

**Example 2: Logistic Regression Models**

Biostatisticians often use logistic regression models to predict whether or not individuals or animals will have a particular binary outcome (yes or no).

For example, researchers can measure a variety of variables including weight, height, age, etc. so they can fit a logistic regression model that tells them the likelihood that a tumor on an animal will be malignant.

**Example 3: Survival Curves**

Biostatisticians often use survival curves to understand the proportion of individuals in a certain population that are expected to be alive after a certain age, or at a given time after contracting some type of disease.

For example, survival curves are frequently used to calculate the probability that individuals will live an additional number of months or years after being diagnosed with a specific type of cancer or other serious illness.

In practice, survival curves are used by biostatisticians, doctors, and epidemiologists all the time to gain a better understanding of the behavior of different viruses, diseases, and illnesses.

**Conclusion**

In conclusion:

The field of **statistics** encompasses a wide variety of methods that can be used with many different types of data.

The field of **biostatistics** is simply the application of these statistical methods to various topics in biology.

**Additional Resources**

The following articles explain the importance of statistics in various fields:

Why is Statistics Important? (10 Reasons Statistics Matters!)

The Importance of Statistics in Business

The Importance of Statistics in Education

The Importance of Statistics in Healthcare