Consider a study testing whether the number of birds resting on streetlights is random. Researchers surveyed 30 randomly chosen streetlights in a city known to have many birds and counted the number of birds resting on each streetlight. Data on the number of birds seen on the lights are shown. (Note: No lights had more than three birds.) Using the ?² value we obtained for a goodness-of-fit test comparing these data to the expectations from a Poisson process and the list of critical values shown, what is the conclusion of our test?
?

\begin{array}{|cc|}\hline \begin{array}{l}\text { Number } \\\underline {\text { birds }}\end{array} & \begin{array}{l}\text { Number } \\\underline {\text { of lights }}\end{array} \\0 & 2 \\1 & 16 \\2 & 10 \\\geq 3 & 2 \\\hline\end{array}

\begin{array}{c}\chi^{2} \text { critical values }\\\text { for } d f=2\\\begin{array}{ll}\text { Sig. level } & \text { Value } \\\hline 0.05 & 5.99 \\0.025 & 7.38 \\0.01 & 9.21\end{array}\end{array}

?
A) Fail to reject the null hypothesis, the birds appear randomly distributed on the streetlights.
B) Fail to reject the null hypothesis, the birds appear non-randomly distributed on the streetlights.
C) Reject the null hypothesis, the birds appear randomly distributed on the streetlights.
D) Reject the null hypothesis, the birds appear non-randomly distributed on the streetlights.

Consider a Situation in Which We Expect One Third of the Observed

Multiple Choice

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