ECOL 182 lab statistics practice

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University Of Arizona *

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182L

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Statistics

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Feb 20, 2024

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Statistics Practice ECOL 182L Lab 3 Assignment - University of Arizona Your name: Claire Montague Team member names: Nathan, Eleanor, Tanya Instructions: ● This is an individual assignment. Do not hesitate to share ideas with your team and TA, but create your own responses. ● Type your responses and upload this document as a Word file (.docx) to the Assignments folder with the same name in D2L. ● Create answers that are concise: specific and to the point. 1. (1 pt) When designing an experiment, why is it important to consider the number of individuals used in an experiment? It is important to consider the number of individuals used in an experiment or the sample size for multiple reasons. The first thing to keep in mind is that a bigger sample is almost always better. A larger sample is more representative of the big picture of the overall population. A larger sample is more likely to capture any natural variation in the population. Ultimately, a larger sample for an experiment will lead to fewer confounding variables. 2. (1 pt) Similarly, why is it important to consider how many replicates are performed across experimental and/or control groups? It is important to consider how many replicates are performed across experimental and control groups for a couple reasons. First, the number of replicates of experimental and control groups should be the same, so you have the same amount of data in your results. It is important to perform replicates of the control group to make sure it is a control, and there were no confounding variables that may have affected the outcome. It is important to perform replicates of the experimental groups to ensure the results you got weren’t due to chance or a confounding variable. It also helps to be able to compare results from multiple replicates of controls and multiple replicates experimental groups because there will be more data to compare, any outliers in data will have less of an effect, and overall it will be more reliable. 3. (1 pt) Factors other than size help determine whether a sample is “good.” If you think about other criteria involved in determining how “good” a sample is, do you think our class height data (Data: Class Height in D2L) represents a “good” sample for the

national height average of adults in the United States? In other words, is our sample representative of the larger population? Why or why not? I think it could be a good representation of the larger population, but it would be a better representative of the population if we had a larger sample. I think that our class sample is a good representation because even though it is a smaller sample, we don’t have any extreme outliers to skew the data. 4. (1 pt) Describe what “standard error of the means” means in your own words. You can provide an example if that helps. Standard error of the means is a measurement of how different the sample mean is from the population mean. The standard error of the means can help determine whether that sample is representative of the overall population.

5. (2 pts) Find Data: Adult Height in the United States in D2L and export a copy you can open in Excel. Use the data to calculate means, standard deviations, and 95% confidence intervals for female adults and male adults. ● When in doubt, round your calculations to three decimal places. Female Adult Height Sample Male Adult Height Sample Mean 161.102 174.895 Standard Deviation 6.345 7.759 95% CI 0.556 0.680 6. (3pts) Using Data: Adult Height in the United States , create a graph comparing the mean height of females adults and male adults. Insert it below. ● Your graph must include 95% CI error bars. ● Include axis labels and measurement units for full credit. ● If your graph is identical to a team member’s graph, you will earn no credit. 7. (1 pt) Focusing on the 95% CI error bars in your graph, explain whether there appears to be a significant difference between average adult female height and average adult male height in the United States. There appears to be a significant difference in the average adult female and average adult male height in the United States because the top of the female 95% CI error bar is below the bottom of the male 95% CI error bar.

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8. (1 pt) Perform a t-test comparing average adult female height and average adult male height from the United States Height Data. Report alpha and your p -value below. alpha = 0.05 p -value = 1.7209 x 10 -118 9. (2 pt) Based on the results of your t-test, is there a statistically significant difference between average female height and average male height in the United States? Briefly explain and provide justification for this answer. Since the p-value is less than or equal to alpha, we reject the null hypothesis. We have sufficient evidence to say that men and women in the United States do not have the same average heights.