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peripatein
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Why would a p-value greater than .95 plausibly indicate that the errors/uncertainties were overestimated, i.e. a small chi-square?
peripatein said:Why would a p-value greater than .95 plausibly indicate that the errors/uncertainties were overestimated, i.e. a small chi-square?
The chi-square test is used to determine whether there is a significant relationship between two categorical variables. The p-value is the probability of obtaining a result at least as extreme as the observed result, assuming the null hypothesis is true. In other words, the p-value indicates the likelihood of obtaining the observed chi-square value by chance alone.
A p-value of less than 0.05 is generally considered statistically significant, indicating that there is a low probability of obtaining the observed chi-square value by chance alone. This suggests that there is a significant relationship between the two variables being studied. A p-value greater than 0.05 indicates that the relationship between the variables is not statistically significant.
No, a high chi-square value does not always indicate a significant relationship. The significance of the chi-square value also depends on the sample size and the number of categories in each variable. A small sample size or a large number of categories can result in a high chi-square value even if the relationship between the variables is not significant.
The p-value decreases as the sample size increases, which means that a larger sample size increases the likelihood of obtaining a statistically significant result. This is because a larger sample size provides more data points, making it easier to detect a significant relationship between the variables.
No, a low p-value does not necessarily mean that there is a strong relationship between the variables. It only indicates that the observed relationship is statistically significant. The strength of the relationship can also be determined by the effect size, which measures the magnitude of the relationship between the variables.