Proceedings of the 2 nd International Symposium on Mastitis and Milk Quality (2001) 357 EVALUATION OF BOVINE TEAT CONDITION IN COMMERCIAL DAIRY HERDS: 3. GETTING THE NUMBERS RIGHT D.J. Reinemann 5 , M.D. Rasmussen 2 , S. LeMire 5, F. Neijenhuis 3 , G.A. Mein 1 , J.E. Hillerton 4 , W.F. Morgan 1 , L.Timms 5 , N. Cook 5, R. Farnsworth 5 , J.R. Baines 4 , and T. Hemling 5 “Teat Club International”, c/o F. Neijenhuis, Research Institute for Animal Husbandry Lelystad, The Netherlands Authors from: Australia 1 , Denmark 2 , The Netherlands 3 , UK 4 , USA 5 Classification of bovine teat condition can be used to assess the effects of milking machines, milking management or environment on teat tissue and the risk of new intra-mammary infections. Veterinarians and others require a simple and reliable method for evaluating teat health in dairy herds. Teat condition can be classified using continuous measures (e.g., teat thickness measured in mm) or categorical scores (e.g., smooth or rough). Categorical data are often presented using a numerical scale (e.g., teat end condition = 1, 2, 3, or 4). This may lead to false statistical conclusions if the relative risk factors are not distributed according to their numerical value. Other common statistical problems of evaluating teat condition in the field are insufficient sample size, using improper statistical tests for skewed data, and samples not representing the population in question. For more information on sample size, statistical inference, and guidelines for the collection of statistically defensible data, see LeMire et al. (1998, 1999). This paper provides simple guidelines for statistical evaluation of the teat conditions (Table 1) proposed in the companion paper by Mein et al. (2001). Sampling all cows: In small herds (up to 100 cows) it may be practical to score the entire herd. In this case the entire population of cows has been measured and the sample mean is equal to the population mean. No additional statistical analysis is required to estimate this true population mean. Statistical analysis is still required, however, to determine if changes in this population mean are significant. Random Sampling: In larger herds, random sampling is desirable in order to reduce the amount of time and resources for estimation of the true mean of the herd. The validity of all statistical analysis is based on a random sample of independent subjects from the entire population of subjects. Random samples should be taken from either all the cows or the production groups of interest. The outcome of the scoring is only valid for the population represented by the sample. One of the most common errors in estimating the condition of a herd based on a sample is lack of randomization. As the percentage of the population sampled decreases, the importance of random sampling increases. Most common statistical tests assume that the measure of interest in the population of subjects is normally distributed about some average value. Since the outcome of teat scoring is a yes/no response the following tables are based on a binomial distribution. False Positive Error: Mein et al. (2001) propose describing the teat condition status of a herd of cows by the proportion of cows that have a particular teat condition classed as abnormal, e.g., more than 20% of cows with rough teat-ends. A statistical test will result in claims that the percentage of cows or quarters in a population differs from some critical value based on measurements of a given sample of the population. A false positive (Type I) error is the claim of difference (more than 20% of cows are affected) when the difference does not exist (20% or fewer cows are affected). The probability of a false positive error is denoted by α. The