![]() The second condition, when we're talking aboutĬhi-squared hypothesis testing, is the large counts. And that would be that there's truly a random sample of games. So you've seen some of them, but some of them are a Statistic that large or greater, let's make sure we meet theĬonditions for inference for a chi-squared goodness-of-fit test. Our chi-squared statistic, and figure out what's the probability of getting a chi-squared And so what he did is he took a sample of 24 games, so n is equal to 24. Another way to think about it is if our P-value is below threshold, we would reject our null hypothesis. Result at least this extreme is low enough, then we would reject our null hypothesis. Null hypothesis is true, the probability of getting a And then his alternative hypothesis would be that his outcomes have not equal not equal probability. Would be that he has that all of the outcomesĪre equal probability. So what would his null hypothesis be? Well, his null hypothesis Because it's a hypothesis that's thinking about multiple categories. Just doing a hypothesis test using the chi-squared statistic. What are the values of the test statistic, the chi-squared test statistic, and P-value for Kenny's test? So pause this video and see He wants to use these results to carry out a chi-squared goodness-of-fit test to determine if theĭistribution of his outcomes disagrees with an even distribution. So out of the 24 games, he won four, lost 13, and tied seven times. So he took a random sample of 24 games and recorded their outcomes. Kenny plays rock-paper-scissors often, but he suspect his own games were not following that pattern. ![]() Game rock-paper-scissors, Kenny expects to win, tie,Īnd lose with equal frequency.
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