Why take averages in experiments

The more experiments completed by the scientist the stronger the principle is for the hypothesis. There are two types of variables when running tests: independent and dependent. An experiment with two groups, such as using water on one set of plants and nothing on a second set, has independent and dependent variables.

The group that receives water, in this example, is the independent variable because it does not depend on happenstance. The scientist applies the water by choice. The dependent variable is the response that is measured in an experiment to show if the treatment had any affect. The lack of water on the set of plants shows whether the application by the scientist changes the outcome so therefore it depends on the independent variable.

This experiment needs to be done more than once due to the potential for variation, meaning some of the plants could have had disease or other outside variable that spoiled the experiment unbeknownst to the scientist conducting the experiment.

The more samples presented at each test the better chance the scientist has of coming to a solid conclusion with little room for error. The award-winning journalist has covered home decor, celebrity renovations, and sat down with reality HGTV stars to discuss the latest trends.

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They made time to make sure that you understood the material and they were there to see to it that you succeeded. In a large set of data like this, instead of analyzing each time recorded, it is easier to analyze the entire set of data. There are three very useful functions for taking the average, or what value is expected for the entire value. Note: All of our examples assume you have 10 data points stored from A1 to A The average of the data is also called the arithmetic mean.

The average is the value that we expect to get when performing a specific trial of an experiment. It is calculated by adding all of the numbers in the data set, then dividing the sum by the number of trials that we took. In this example, we could take the sum of the cells A1 through A10 and divide by 10 the number of trials taken and this would give us the average, or the arithmetic mean.

The average is useful because without taking another trial, we can have a guess as to what the outcome should be or at least pretty close. If you have a few data points and would like to find the average by hand, you can do this simply by adding the values and dividing by the number of data points.

The median is useful for data sets that are very lopsided. For example, if there are 9 trials that are measured around 4. Therefore, in this case, the median is more useful. The mode of the data is the value which appears most frequently. The mode is useful because it can be considered in what value we can expect in another trail.