Analyzing Data: Part 1

One of the most important ways that ecologists (and many other scientists) use math is to help them to analyze their data. We have already talked a little bit about how scientists use tables and graphs to help display their results. In addition, scientists often need to use statistics to help them test their hypotheses.

Really understanding how to use statistics to help test hypotheses usually requires taking a Statistics class. Let me say right up front that this is not a statistics course. Instead the goal of this portion of the course is to illustrate some of the steps that scientists use to analyze their data. Some of the material that we cover will probably be a bit advanced for you to use in the middle school classroom, but most of what we discuss can easily be scaled to the appropriate grade level. However, because you will be involved in some sort of research project this summer, you should all benefit from learning how scientists use statistics to test hypotheses. Who knows, you may even be able to test out your new knowledge of hypothesis testing on your projects.

During this portion of the coure we will use "The Process of Science", a lab manual that I wrote when I was coordinating the labs for all of the non-majors Biology courses taught at TTU (Plant Biology, Animal Biology, and Ecology & Environmental Problems). My suggestions on how to use the manual are written on the first page. I will let you read Chapter 1 on your own. In class we will start with Chapter 2 because that is the first chapter that involves math.

Chapter 2: The Importance of Quantification

Expected Learning Outcomes
By the end of this course, a fully engaged student should be able to
- identify variables that are easily quantified and variables that are not so easily quantified.
- identify the dependent and independent variables
- ask and answer questions using the three most common approaches that scientists use to analyze data-
a) comparing means
b) testing for correlations between two quantifiable variables
c) testing for associations between two categorical variables
- distinguish between positive and negative correlations.

Chapter 3: Hypothesis Testing

Expected Learning Outcomes
By the end of this course a fully engaged student should be able to
- explain when we must estimate the "true answer" using a sample
- explain why we can not prove that a hypothesis is true when we estimate the true answer with a sample
- apply the steps in the hypothesis testing protocol
- determine when you need to use statistics to test hypotheses