With this tool you can either input a value for 'z' and find what proportion of the normal distribution falls within the area it defines ('p'), or you can input 'p' and find what value(s) of 'z' define that proportion. The relevant proportion of the distribution is indicated by the lined areas. If you randomly sample from a population that is normally distributed then p is also the probability that the score you obtain will fall within the lined areas.
You have four choices:
With this tool you can either input a value for 't' and find what proportion of the t distribution falls within the area it defines ('p'), or you can input 'p' and find what value(s) of 't' define that proportion. The relevant proportion of the distribution is indicated by the shaded areas. The 'degrees of freedom' come from the type of data analysis you are performing.
If you calculate the value of t from your data, and if the assumptions underlying the applicability of the t distribution are met, then p is the 'p value' that goes with your analysis. This is the probability that you would have obtained the value of t that you did, or one even further from zero, if the null hypothesis were true.
You have three choices:
With this tool you can either input a value for 'F' and find 'p' (the proportion of the F distribution that falls beyond the value of F), or you can input 'p' and find what value of 'F' defines that proportion. The relevant proportion of the distribution is indicated by the shaded area. The 'degrees of freedom' come from your data analysis. The 'df Numerator' is the df associated with the Mean Square in the numerator of the F ratio. In a simple one-factor ANOVA this would be df_between. The 'df Denominator' is the df associated with the Mean Square in the denominator of the F ratio. In a simple one-factor ANOVA this would be df-within.
If you calculate the value of F from your data, and if the assumptions underlying the applicability of the F distribution are met, then p is the 'p value' that goes with your analysis. This is the probability that you would have obtained a value of F that large or larger if the null hypothesis were true.
With this tool you can either input a value for 'χ²' (Chi Square) and find 'p' (the proportion of the χ² distribution that falls beyond the value of χ²), or you can input 'p' and find what value of 'χ²' defines that proportion. The relevant proportion of the distribution is indicated by the shaded area. The 'degrees of freedom' come from your data analysis.
If you calculate the value of χ² from your data, and if the assumptions underlying the applicability of the χ² distribution are met, then p is the 'p value' that goes with your analysis. This is the probability that you would have obtained a value of χ² that large or larger if the null hypothesis were true.