

Materials
How does making a list of the materials that
you will require for your experiment help? Very simply, this will ensure that
you have all of the materials ready when you need them. It's important to be
specific. You should mention dimensions and weight where necessary - approximate
measurements may suffice for some of your projects.
Here's a sample material list from our sunscreen lotion experiment. Materials list- 16 sheets of glass (approximately 550 mm in length x 550 mm wide).- 5 brands of sunscreen with SPF 15 - 5 brands of sunscreen with SPF 30 - 5 brands of sunscreen with SPF 50 - 1 UV meter to measure the UV index readings - 1 bottle of glass cleaner - A box of disposable gloves (at least 15 gloves) - 1 piece of cloth - A pair of thick safety gloves - 1 wooden box with no covering on the top (approximately 500 mm in length x 500 mm wide)
Conducting the experiment
As you proceed with your experiment, these are a few things that you need to remember:
1. Carefully and accurately record your observations in your project journal 2. All measurements should be precise and carefully recorded, making the necessary adjustments for systematic errors. 3. All data should be meticulously recorded in a table/chart. Common Mistakes in Applying the Scientific Method
1. Failing to conduct the experiment
Believe it or not, the most common mistake made by scientists is to accept the hypothesis for an explanation of a phenomenon, without performing experimental tests! Sometimes "common sense" and "logic" tempt us into believing that no test is needed! Amazing huh? 2. Ignoring relevant data Another common mistake is to ignore or "rule out" data which does not support the hypothesis. As a scientist, you should be completely open to the possibility that the hypothesis is correct or incorrect. Sometimes, however, a scientist may have a strong belief that the hypothesis is true (or false). Also, often, a scientist may be under pressure for various reasons to get a specific result. In such cases, there is a tendency to find "something wrong" with data which does not support the scientist's expectations, while data which does agree with those expectations may not be challenged as carefully. A responsible scientist must evaluate all data objectively and honestly, without bias. |
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