Mastery Check – Understanding the Data in Statistics
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Question 1 of 5
1. Question
Match the description of the data with the appropriate distribution
Sort elements
- mass of coconuts in grams
- Snout to vent length of Western Fence Lizards
- Counts of flowering plant abundance between study plots
- Number of species found in different tidepools
- Counts of frogs found only in Site A and not found in Site A
- Presence/absence of ornamental feathers in Birds of Paradise
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Normal Distribution
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Normal Distribution
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Poisson Distribution
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Poisson Distribution
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Binomial Distribution
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Binomial Distribution
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Question 2 of 5
2. Question
You collect data on the amount of time Bonobos spend grooming related individuals. You run a Shapiro-Wilk test to determine if your data is normally distributed, and receive a p-value of .35. What can you claim about your data?
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Question 3 of 5
3. Question
You collect data on the number of flowers visited by European Honeybees between three different sites. You wish to run an ANOVA on this data, but wonder about the Homogeneity of Variances assumption. You run a Bartlett test and receive a p-value of .03. How would you interpret this result?
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Question 4 of 5
4. Question
You are worried about the independence assumption for some of your statistical models. You decide to check by calculating the correlation coefficient between all combinations of 3 variables: Temperature – Humidity – Elevation
The results of each correlation coefficient are as follows:
- Temperature – Humidity = .06
- Temperature – Elevation = .6
- Humidity – Elevation = .05
How would we interpret the correlation coefficient for the Temperature-Elevation correlation test? Assume the correlation is significant.
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Question 5 of 5
5. Question
You collect data on the diameter of sea stars found in tidal pools along the western coast. You pool the data together and visualize the data distribution in R. You are worried as the data appears to have a slight right skew. You know the statistical test you want to run has the assumption of normality, so you decide to run a Shapiro-Wilk test and receive a p-value of .55. From this, you decide to log transform the data. Running the Shapiro-Wilk test again you now receive a p-value of .00001.
Was this procedure correct? Why or why not?
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