Undergrad Using interpolation to calculate p-values from t-table?

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The discussion centers on using interpolation to calculate p-values from t-tables, acknowledging the limitations of t-tables in providing p-values for every t-score. Linear interpolation is mentioned as a common method, but participants express interest in exploring alternative methods due to the non-linear nature of the t-distribution. A quadratic interpolation approach is introduced, involving functions that can provide more accurate p-values by considering multiple measured values. The conversation highlights the need for better understanding and techniques in statistical analysis. Overall, the thread emphasizes the importance of interpolation methods in enhancing the precision of p-value calculations.
tomizzo
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Hi there,

I've started learning the concept of t-tables and have a question regarding methods to find p-values.

I realize that the t-table is limited in providing p-values for every possible t-score. Instead, we must rely on interpolation to attempt to get more precision on the p-value. I've read that linear interpolation is a common method for extending the range of p-values, but are there alternative interpolation methods?

The t-distribution is not exactly linear, thus there must be better interpolation methods/transformations available?

I'm having a difficult time with Google on this one, so I appreciate any help!
 
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Not knowing what a t or p table actually is, I'll jump right in. Say we have two measured values, ##p_1## and ##p_2## taken at ##t_1## and ##t_2##. Let us assume that ##t_1\lt t \lt t_2##. Then the linear interpolated value, ##p##, is given by,

##p=p_1+\frac{p_2-p_1}{t_2 - t_1}(t-t_1)##​
 
Reading comprehension not my strong suit, I'll try again,

Let
##F_1(t)=\frac{(t-t_2)(t-t_3)}{(t_1-t_2)(t_1-t_3)}##
##F_2(t)=\frac{(t-t_1)(t-t_3)}{(t_2-t_1)(t_2-t_3)}##
##F_3(t)=\frac{(t-t_1)(t-t_2)}{(t_3-t_1)(t_3-t_2)}##

##p(t) = F_1(t)p_1+F(t)p_2+F(t)p_3##

##p(t)## is a quadratic interpolation. Note that ##F_i(t_j)=\delta_{ij}##​
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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