[size=150] A scientist compares methane levels on Mars at several locations, in parts per billion, taken at 2 different times during the Martian day. The difference between the means of the methane levels for the two different times is -0.08 parts per billion. The scientist uses simulations to create a randomization distribution to try to determine the likelihood that the results happened by chance. The histogram represents the results of the 200 trials from the simulations.[/size][br][br][img]data:image/png;base64,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[/img][br]According to the randomization distribution, is the difference in means due to chance or the time at which the measurements were made? Explain your reasoning.

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[/img][br][/td][td][size=150]A biologist is studying the research question, [br]“Do eggs from the common gull, a type of bird, [br]take longer to hatch at 85 degrees Fahrenheit or at 87 degrees[br]Fahrenheit?” They design an experiment in which they select [br]20 eggs and assign those eggs to 2 groups of 10 eggs each [br]at random.[br][br]The eggs in the first group are placed in an environment [br]that is held at a constant temperature of 85 degrees Fahrenheit, [br]and the eggs in the second group are placed in an environment [br]that is held at a constant temperature of 87 degrees Fahrenheit. [br]The hatching times, in days, for each group are displayed [br]in the table.[br][br]The mean hatching time for group 1 is 25.8 days and [br]the mean hatching time for group 2 is 25.2 days.[/size][/td][/tr][/table][br]How could the biologist get a randomization distribution to compare the two groups?[br]

How would the biologist use the randomization distribution to determine whether the difference between the mean hatching time for group 1 and the mean hatching time for group 2 is due to chance?[br]

[size=150]Diego rolls a standard number cube 10 times and adds the values to get a sum of 40. Is that unusually high? Priya simulates rolling the number cube 10 times on a computer and adds the values. She repeats that process 100 times and creates a histogram of the results.[/size][br][br][img]data:image/png;base64,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[/img][br]Based on the histogram, does 40 seem unusually high? Explain your reasoning.[br]

[size=100]The mean of Priya’s simulations is a sum of 35, and the standard deviation is 5.72. [/size][br]Using a normal distribution as an approximation of this distribution, what is the probability that a person would roll a sum greater than 40?[br]

[size=150]Why do we use randomization distributions?[/size]

Which of these values would be most useful for determining whether the difference in means is likely due to the treatment? Explain your reasoning.[list][*]interquartile range[/*][*]median[/*][*]standard deviation[/*][*]range[/*][/list]