Moonbear said:Pearson's correlation coefficient is a parametric statistic. If your data require a non-parametric test (how did you determine this?), then you can't use Pearson's correlations, but should instead be using Spearman's correlation coefficient, which is the non-parametric counterpart to Pearson's. But, nobody will be able to help you beyond that without more information about the data set you're working with. What's your sample size? Do you have continuous or integer data, random or fixed variables, etc? Basically, without knowing your experimental design, nobody can recommend an appropriate statistical test.
I believe you can; as explained in this link or http://faculty.vassar.edu/lowry/rsig.html. (Both from a Yahoo! search on significance test for correlation coefficient.)sobored said:Thanks for helping me out.
Both groups have 35 samples and the values are continues. Can i use student T test to predict this p-value, assumming that i use the parametric way?
A p-value is a statistical measure that helps determine the likelihood of obtaining a certain result by chance alone. It is often used in hypothesis testing to determine if a null hypothesis can be rejected.
A p-value is calculated by determining the probability of obtaining a test statistic at least as extreme as the one observed, assuming the null hypothesis is true. This is typically done using a statistical software or by referring to a p-value table.
A p-value of 0.05, or 5%, means that there is a 5% chance of obtaining the observed result by chance alone if the null hypothesis is true. In other words, if the p-value is less than 0.05, it is considered statistically significant and the null hypothesis can be rejected.
The interpretation of a p-value depends on the context of the study and the significance level chosen. Generally, a p-value less than the chosen significance level (usually 0.05) indicates that there is enough evidence to reject the null hypothesis. A p-value greater than the significance level suggests that the observed results could be due to chance and the null hypothesis cannot be rejected.
P-values are not without limitations and should be interpreted with caution. They do not provide information about the size of the effect or the practical significance of the results. Additionally, p-values do not account for multiple comparisons, which can inflate the likelihood of obtaining a significant result by chance alone. It is important to consider the context of the study and the p-value in conjunction with other measures of statistical significance.