Public Health Biostatistics I | © Dennis Dantic |

Introduction to Public Health Biostatistics

Public Health Biostatistics is an introduction to the application of data analysis course that makes use of graphical and numerical techniques to study patterns and departures from patterns in the field of public health. The student studies randomness with emphasis on understanding variation, collects information in the face of uncertainty, checks distributional assumptions, tests hypotheses, uses probability as a tool for anticipating what the distribution of data may look like under a set of assumptions, and uses appropriate statistical models to draw conclusions from data. The use of technology (computers or graphing calculators) will be required in certain applications.

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What is Biostatistics?
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Biostatistics (a blend of the words biology and statistics; sometimes referred to as biometry or biometrics) is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, public health and agriculture; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results.

The Science of Biostatistics involves

•Data Collection

•Data Analysis

•Data Interpretation

•Data Presentation

We can use biostatistics to better understand the relationship of the environment, disease (and other health related issues and events) and the host.

For example, if we notice that a lot of students in our class seem tired, we might be interested in finding out the average number of hours each student is sleeping every night

Data Collection

First, data must be collected.

In the above example of tired students, if we want to find out the average amount of time students sleep per night, we could collect data from our classmates.

First, each person would keep a diary, in which he or she writes down the number of hours he or she sleeps every night for two weeks. Then we would ask each person to report his or her results

Data Analysis

Next, the data is analyzed.

We could analyze the data from our sample class by finding the average time each student slept over a certain period of time, say two weeks. The following table shows how much time one particular student, Susan, slept each night for two weeks.

Data interpretation

Then, data needs to be interpreted.

One interpretation of our sample data might be that most students sleep less than 8 hours per night on the week nights and more on weekends.

Data Presentation

Finally, data can be presented.

We could present the sleep averages for all the students in a line graph (see below), a graphic representation where the occurrences of data are represented by lines plotted in a graph.

Like most people, you probably feel that it is important to "take control of your health and as whole your life." But what does this mean? Partly it means being able to properly evaluate the data and claims that bombard you every day. If you cannot distinguish good from faulty reasoning, then you are vulnerable to manipulation and to decisions that are not in your best interest. Biostatistics provides tools that you need in order to react intelligently to information you hear or read. In this sense, Biostatistics is one of the most important things that you can study in public health.

To be more specific, here are some claims that we have heard on several occasions. (We are not saying that each one of these claims is true!)

•4 out of 5 dentists recommend Dentyne

•Almost 85% of lung cancers in men and 45% in women are tobacco-related.

•Condoms are effective 94% of the time.

•Native Americans are significantly more likely to be hit crossing the streets than are people of other ethnicities.

•People tend to be more persuasive when they look others directly in the eye and speak loudly and quickly.

•Women make 75 cents to every dollar a man makes when they work the same job.

•A surprising new study shows that eating egg whites can increase one's life span.

•People predict that it is very unlikely there will ever be another baseball player with a batting average over 400.

•There is an 80% chance that in a room full of 30 people that at least two people will share the same birthday.

•79.48% of all statistics are made up on the spot.

All of these claims are statistical in character. We suspect that some of them sound familiar; if not, we bet that you have heard other claims like them. Notice how diverse the examples are. They come from psychology, health, law, sports, business, etc. Indeed, data and data-interpretation show up in discourse from virtually every facet of contemporary life. Statistical data are often presented in an effort to add credibility to an argument or advice. You can see this by paying attention to television advertisements. Many of the numbers thrown about in this way do not represent careful statistical analysis. They can be misleading, and push you into decisions that you might find cause to regret. For these reasons, learning about statistics is a long step towards taking control of your life. (It is not, of course, the only step needed for this purpose.)

You can take the first step right away. To be an intelligent consumer of statistical data, your first reflex must be to question the statistics that you encounter. The British Prime Minister Benjamin Disraeli famously said, "There are three kinds of lies -- lies, damned lies, and statistics." This quote reminds us why it is so important to understand statistics. So let us invite you to reform your statistical habits from now on. No longer will you blindly accept numbers or findings. Instead, you will begin to think about the numbers, their sources, and most importantly, the procedures used to generate them.

We have put the emphasis on defending ourselves against fraudulent claims wrapped up as statistics. We close this section on a more positive note. Just as important as detecting the deceptive use of statistics is the appreciation of the proper use of statistics especially biostatistics in relations to various health topics. You must also learn to recognize statistical evidence that supports a stated conclusion. Statistical applications are all around you, sometimes used well, sometimes not. We must learn how to distinguish the two cases.

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