What is the difference between chi square and p value

It is used to determine whether sample data are consistent with a hypothesized distribution. For example, suppose a company printed baseball cards. We could gather a random sample of baseball cards and use a chi-square goodness of fit test to see whether our sample distribution differed significantly from the distribution claimed by the company. The sample problem at the end of the lesson considers this example. The chi-square goodness of fit test is appropriate when the following conditions are met:.

This approach consists of four steps: 1 state the hypotheses, 2 formulate an analysis plan, 3 analyze sample data, and 4 interpret results. Every hypothesis test requires the analyst to state a null hypothesis H o and an alternative hypothesis H a. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false; and vice versa.

The likelihood chi-square statistic is Therefore, at a significance level of 0. To determine which variable levels have the most impact, compare the observed and expected counts or examine the contribution to chi-square.

By looking at the differences between the observed cell counts and the expected cell counts, you can see which variables have the largest differences, which may indicate dependence.

You can also compare the contributions to the chi-square statistic to see which variables have the largest values that may indicate dependence.

In our example, the X 2 value of 1. By convention biologists often use the 5. Any deviations greater than this level would cause us to reject our hypothesis and assume something other than chance was at play. See red circle on Fig 5. If your chi-square calculated value is greater than the chi-square critical value, then you reject your null hypothesis.

If your chi-square calculated value is less than the chi-square critical value, then you "fail to reject" your null hypothesis. Fig 5 : Finding the probability value for a chi-square of 1. First read down column 1 to find the 1 degree of freedom row and then go to the right to where 1. This corresponds to a probability of less than 0. Therefore in our tomato breeding example, we failed to reject our hypothesis that resistance to bacterial spot in this set of crosses is due to a single dominantly inherited gene Rx We can assume that the deviations we saw between what we expected and actually observed in terms of the number of resistant and susceptible plants could be due to mere chance.