It is hypothesized that a CroOkeD Die is actually a fair die. Two crooked dice are pictured below.

You decide to perform goodness of fit test to decide whether a CroOkeD Die is fair or not. You decide to use a significance level of 0.05.

To perform this test, you need to collect some data. The data is included in the following video.

Based on this data, what is the p-value? (21:80)

Question

It is hypothesized that a CroOkeD Die is actually a fair die. Two crooked dice are pictured below.

You decide to perform goodness of fit test to decide whether a CroOkeD Die is fair or not. You decide to use a significance level of 0.05.

To perform this test, you need to collect some data. The data is included in the following video.

Based on this data, what is the p-value? (21:80)

BlackTom AI · Accepted Answer

We are given a scenario where a goodness-of-fit test is used to decide if a Crooked Die is fair or not. The data to base the test on are described as (21:80) in the prompt, and the reported p-value is 0.011.

First, outline the test idea: for a fair six-sided die, each face (1 through 6) is expected to occur with probability 1/6, given a total sample size N. The goodness-of-fit (chi-square) test compares observed counts O1,…,O6 to expected counts E1,…,E6 where Ei = N/6 for all i.

Step-by-step reasoning about the p-value concept:
- The p-value is the probability, under the null hypothesis (the die is fair), of obtaining a chi-square statistic as extreme or more extreme than the one observed from the data.
- A small p-value indicates that the observed distribution of face counts is unlikely under the assumption of fairness, providing evidence against the null.

About the specific value 0.011:
- If the p-value is 0.011, this means there is about a 1.1% chance of seeing such a discrepancy between observed and expected counts (or a more extreme one) if the die were actually fair.
- With a significance level of 0.05, a p-value of 0.011 would lead to rejection of the null hypothesis at the 5% level. In other words, the data provide statistically significant evidence that the die is not fair, assuming the test’s assumptions hold.

Important caveats about interpreting this p-value:
- The chi-square test relies on sufficiently large expected counts (commonly all Ei ≥ 5). If the data (21:80) yield small Ei for some faces, the approximation to the chi-square distribution may be questionable, and a exact multinomial or G-test or simulation-based approach might be more appropriate.
- The total sample size and distribution of counts matter. If N is small or highly imbalanced, the p-value might be unstable. The reported p = 0.011 is only as reliable as the data and the test’s assumptions.
- If multiple tests were performed or if data were analyzed in several ways (e.g., different grouping or alternative hypotheses about bias), the p-value should be interpreted with adjustment for multiple comparisons.

Connecting to the given data snippet (21:80):
- The pair (21:80) likely represents two observed totals or a compressed summary from the video data. To obtain the p-value yourself, you would: (a) compute N = sum of observed counts across all faces, (b) compute Ei = N/6 for each face, (c) compute the chi-square statistic = sum[(Oi − Ei)^2 / Ei] across faces, (d) find the p-value as P(Chi-square with df = k−1 − r ≥ observed) where k is the number of face categories (6) and r is the number of estimated parameters (here typically 0 for a fully specified fair die), i.e., df = 5, unless constraints reduce df.

In summary:
- A p-value of 0.011 suggests statistically significant evidence against fairness at the 0.05 level, assuming the test conditions are satisfied. 
- The conclusion would be that the Crooked Die is unlikely to be fair, based on this test alone.
- Always check the assumptions (expected counts, independence, correct model) before fully trusting the result, and consider alternative tests if those assumptions are violated.

BTRY3010/BTRY5010/STSCI2200/STSCI5200 Lecture #27 (Pages 130 - 135 )

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