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2.analysis_and_write_up

# 2.analysis_and_write_up - 4th Lab Data Analysis Now that...

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4 th Lab; Data Analysis Now that you have completed these labs and collected all of the data, you need to analyze your results. Remember that you need an objective and quantitative method of making your decision about your experimental results. Our goal is to decide whether or not our data support our alternate hypotheses. 1. Null and Alternate Hypotheses Fill in the chart below using your previous laboratory handouts and discussion. You won’t be able to fill in the last column just yet. Parameter Null Hypothesis Alternate Hypothesis Alt. Hypothesis Supported? Rejected? Plants Table 1: summary of your null and alternate hypotheses 2. Why not just compare the averages or means? It is tempting to look at the average or mean amount of plant growth (or the other factors) to make your decision as to which hypothesis to accept. The problem with this approach is that it doesn’t take into account the variability of the data. These graphs illustrate the problem with simply comparing the average plant growth for each treatment (control, medium and high fertilizer) for each fertilizer treatment. For each of the following graphs, let’s pretend that they are measuring the amount of plant growth and that X1, X2, and X3 are each the average amount of growth for the three different treatments. One possible scenario is represented by Figure 1. There is a clear difference between the average amount of growth between the three treatments. Furthermore,even though there is small amount of overlap between the three treatments, we can still see that there are three distinct ‘groups’ of data. In this scenario , the data clearly support the Alternate Hypothesis ( that there is a difference in plant growth with different fertilizer treatments) 1

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We can start to see the problem with just comparing the means when we look at Figure 2. The average amount of growth is different for each treatment, but the data for each group overlaps the other significantly. This overlap is because there is so much variation in the data for each treatment (control, medium, high fertilizer), that you can’t decide if these are really three different ‘groups’ of data, or really one highly variable grouping Our concern is that if we get a scenario like Figure 2, that we might have enough variation in our data that it is really Figure 3. In Figure 3, there is no difference at all between the three treatments and we would have to go with our null hypothesis. The tricky problem is differentiating between these three scenarios: Figure 1, 2, and 3.
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