Two-stage and sequential sampling for estimation and testing with prescribed precision. Further, the sampling units are tested in sequence until the data gathered is sufficient for estimating the parameters and can be subjected to testing hypotheses ( Zacks, 2017 Zacks, S. In sequential sampling, the number of sample units varies, which can help reduce sampling time with no loss of reliability. Sequential Methods and their Applications. Sequential sampling is a faster, reliable and more efficient alternative to the fixed-sample-size method ( Mukhopadhyay and Silva, 2009 Mukhopadhyay, N. However, because of the expediency of the planting time, seeds need fast decisions so that they can be released for cultivation or commercialization.Д¬onventional sampling methods are based on a fixed number of sampling units. Widely used in agricultural research, these sampling rules originated in quality control techniques. A sample for the test must be collected according to the guide provided by the International Seed Testing Association ( ISTA, 2008 International Seed Testing Association. Seed Science and Technology 38: 421-431.). Staging coffee seedling growth: a rationale for shortening the coffee seed germination test. Preparation of coffee seeds to assess viability using the tetrazolium test. The tetrazolium test is a method for quickly assessing seed viability and vigor ( Clemente et al., 2011 Clemente, A.C.S. However, the Bayesian method required, on average, only 89 samples to reach this value while the traditional estimation method needed as many as 200 samples.Д«eta distribution seed analysis sampling coffee prior distribution The results confirm that the Bayesian sequential estimation can indeed be used for the tetrazolium test: the average percentage of viability obtained with the conventional frequentist method was 88 %, whereas that obtained with the Bayesian method with both priors was 89 %. To estimate the lowest risk, we computed the Bayes risks, which provided us with a basis for deciding whether or not we should continue the sampling process. We used the Beta prior distribution and, using data obtained from sample lots of Coffea arabica, determined its hyperparameters with a histogram and OвЂ™Hagan's methods. Therefore, we used the Bayesian sequential approach to estimate the percentage of viable coffee seeds submitted to tetrazolium testing, and we incorporated priors with information from other analyses of crops from previous years. Bayesian statistics can also help, if we have sufficient knowledge about coffee production in the area to construct a prior distribution. When data obtained from an experiment are analyzed sequentially the analysis can be terminated when a particular decision has been made, and thus, there is no need to pre-establish the number of seeds to assess. These tests may be improved by sequential sampling, which does not work with fixed-size samples. Tetrazolium tests use conventional sampling techniques in which a sample has a fixed size.

Sequential testing beliefs