Labs 1-2 - 01/02_Thazhath Labs 1&2.qxd 8/15/07 8:06 AM Page...

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In this lab, you will learn how to: © Use the general guidelines of the scientific method to formulate and test a hypothesis. © Collect and graph data. © Read data tables and make interpretations of those data. © Identify positive and negative controls, and their absence. Objectives 1 LABS THE SCIENTIFIC METHOD AND DATA ANALYSIS I NTRODUCTION The ability to collect data, assemble them into a useable form, and then draw conclusions from them is a fundamental practice in science. This process is often subject to the scrutiny of other scientists and the general public with the possible endpoint being adoption of drugs for therapeutic use, the develop- ment of government policies and regulations, and an understanding of natural processes that lead to or prevent disease states. In this lab, you will learn how to record trends in data, interpret them, and identify where experimental con- trols are missing. (It is recommended that you review pages 19–26 in your Campbell textbook.) T HE S CIENTIFIC M ETHOD AND E XPERIMENTAL D ESIGN Science begins with the observation of some phenomenon, the workings and subtleties of which are investigated by carefully designed tests that yield results subjected to statistical analysis. The first step in this process is for the investiga- tor to frame their hypothesis. A hypothesis is simply a formal statement of the investigator’s expectation, with the caveat that a scientific hypothesis must be disprovable (i.e., that some observation(s) leads us to reject this hypothesis). 1 & 2 01/02_Thazhath Labs 1&2.qxd 8/15/07 8:06 AM Page 1
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When you decide to test a hypothesis, you will need to make sure that any observations you make will have some kind of standard for comparison before making any conclusions. The null hypothesis states that there is no difference between the data sets you compose, and that any observed differences ARE due only to chance. If your data show a significant difference (see the section below on interpreting statistical tests) then the null hypothesis is rejected, and you must conclude that any observed differences in the data set are real. For example, if you wanted to test the hypothesis that a new medicine, drug-X, acts to relieve headaches, you might first recruit a sample of volunteers from the population. While you could give drug-X to everybody in your sample and ask them whether or not it seemed to help, a more powerful experimental design would be to split your pool of volunteers into two, randomly-selected sub-samples and give drug-X to one sample and not to the other. The sample being manipulated, that is, the group receiving drug-X, is referred to as the experimental sample; the group not under manipulation is the control sam- ple. You might also refer to what you are doing to the experimental sample as a treatment. Before reading on, ask yourself what you think the null hypothesis is. The null hypothesis, sometimes denoted with the shorthand H 0 , is that there will be no significant difference between the experimental and control sam- ples. If you really think drug-X is a painkiller, you do not really think that the
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This note was uploaded on 10/20/2009 for the course COE 1 taught by Professor Many during the Spring '09 term at Georgia Institute of Technology.

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Labs 1-2 - 01/02_Thazhath Labs 1&2.qxd 8/15/07 8:06 AM Page...

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