TABLES OF P-VALUES FOR t- AND CHI-SQUARE REFERENCE DISTRIBUTIONS. W. W. Piegorsch
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1 TABLES OF P-VALUES FOR t- AND CHI-SQUARE REFERENCE DISTRIBUTIONS by W. W. Piegorsch University of South Carolina Statistics Technical Report No Q05-3 August 2002 Department of Statistics University of South Carolina Columbia, SC 29208
2 TABLES OF P-VALUES FOR t- AND CHI-SQUARE REFERENCE DISTRIBUTIONS Walter W. Piegorsch Department of Statistics University of South Carolina Columbia, SC INTRODUCTION An important area of statistical practice involves determination of P-values when performing significance testing. If the null reference distribution is standard normal, then many standard statistical texts provide a table of probabilities that may be used to determine the P-value; examples include Casella and Berger (1990), Hogg and Tanis (2001), Iman (1994), Moore and McCabe (1993), Neter et al. (1996), Snedecor and Cochran (1980), Sokal and Rohlf (1995), and Steel and Torrie (1980), among many others. If the null reference distribution is slightly more complex, however, such as a t-distribution or a 2 -distribution, most standard textbooks give only upper- critical points rather than actual P-values. With the advent of modern statistical computing power, this is not a major concern; most statistical computing packages can output P-values associated with the test statistics they calculate, and can even give upper tail areas (which are often equivalent to or components of a required P-value). Nonetheless, it is useful to have available a table of P-values for settings where computer access may not be available. Towards that end, this work provides a short set of tables for t- and 2 -based P-values. P-VALUES Defined simply, a P-value is a data-based measure that helps indicate departure from a specified null hypothesis, H o, in the direction of a specified alternative H a. Formally, it is the probability of recovering a response as extreme as or more extreme than that actually observed, when H o is true. (Note that more extreme is defined in the context of H a. For example, when testing H o : = o vs. H a : > o, more extreme corresponds to values of the test statistic supporting > o.) In Tables 1 and 2, below, P-values are given for upper tail areas for central t- and 2 - distributions, respectively. These have the form P[t( ) > u] for the t-tail areas and P[ 2 ( ) > c] for the 2 -tail areas, where is the degree of freedom parameter for the corresponding reference distribution. Enter the tables with the argument u or c as the observed (positive) value of the test statistic and with degrees of freedom. REFERENCES Casella, G., and Berger, R. L. (1990). Statistical Inference, 1st Edn. Belmont, CA: Duxbury Press. Hogg, R. V., and Tanis, E. A. (2001). Probability and Statistical Inference, 6th Edn. Upper Saddle River, New Jersey: Prentice Hall. Iman, R. L. (1994). A Data-Based Approach to Statistics. Belmont, CA: Duxbury Press. Moore, D. S., and McCabe, G. P. (1993). Introduction to the Practice of Statistics, 2nd Edn. New York: W.H. Freeman & Co. Neter, J., Kutner, M. H., Nachtsheim, C. J., and Wasserman, W. (1996). Applied Linear Statistical Models, 4th Edn. Chicago: R.D. Irwin. Snedecor, G. W., and Cochran, W. G. (1980). Statistical Methods, 7th Edn. Ames: Iowa State University Press. Sokal, R. R., and Rohlf, F. J. (1995). Biometry, 3rd Edn. New York: W.H. Freeman & Co. Steel, R. G. D., and Torrie, J. H. (1980). Principles and Procedures of Statistics: A Biometric Approach, 2nd Edn. New York: McGraw-Hill.
3 Table 1. One-sided P-values from t( ) distribution: P[t( ) > u]. u Note: <.001 indicates P<
4 Table 1 (cont d). One-sided P-values from t( ) distribution: P[t( ) > u]. u < u Note: <.001 indicates P<.001.
5 Table 1 (cont d). One-sided P-values from t( ) distribution: P[t( ) > u]. u <.001 < u Note: <.001 indicates P<.001.
6 Table 1 (cont d). One-sided P-values from t( ) distribution: P[t( ) > u]. u < <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 Note: <.001 indicates P<
7 Table 2. One-sided P-values from 2 ( ) distribution: P[ 2 ( ) > c]. c
8 Table 2 (cont d). One-sided P-values from 2 ( ) distribution: P[ 2 ( ) > c]. c < < < < < < < < < < < < < < < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 <
9 Table 2 (cont d). One-sided P-values from 2 ( ) distribution: P[ 2 ( ) > c]. c <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 c
10 Table 2 (cont d). One-sided P-values from 2 ( ) distribution: P[ 2 ( ) > c]. c < < < < < < <
11 Table 2 (cont d). One-sided P-values from 2 ( ) distribution: P[ 2 ( ) > c]. c < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 < <.001 <.001 <.001 <.001 <.001 <.001 <.001 <.001 <
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