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1. If you have just collected the data, it is always a good idea to look first at the appropriate summary statistics for each variable in the file. For example, you may want to compute frequency distributions for all variables to identify outliers.
2. Usually, data are collected to test specific hypotheses or models concerning some variables. For example, we may want to find out whether men are more likely to agree with an item on a questionnaire than women. More complicated hypotheses may pertain to the nature of relationships between variables, for example, whether it is linear or non-linear.
3. If you have no specific hypotheses, and the nature of the study is exploratory, you may want to use some techniques that are designed to help 'make sense' of the data, that is, to detect patterns, clusters, etc.
4. The fourth option (quality control/improvement) will guide you through the various techniques for industrial quality control, experimentation, and reliability analysis.
5. The techniques of statistical power analysis, sample size estimation, and advanced techniques for confidence interval estimation are discussed here. The main goal of first two techniques is to allow you to decide, while in the process of designing an experiment, (a) how large a sample is needed to allow statistical judgments that are accurate and reliable, (b) how likely your statistical test will be to detect effects of a given size in a particular situation. The third technique is useful in implementing objectives (a) and (b) above, and in evaluating the size of experimental effects in practice.
6. Entries in the Statistical Glossary are taken from the Electronic Manual of STATISTICA and may contain elements that refer to specific features of the STATISTICA system.
7. As compared to probability calculators, the traditional format of distribution tables like those presented here, has the advantage of showing simultaneously many values and thus allowing the user to examine and quickly explore ranges of probabilities.
8. StatSoft offers a variety of Enterprise-wide software solutions, including STATISTICA Enterprise-wide Data Mining System, STATISTICA Enterprise-wide Data Analysis System and STATISTICA Enterprise-wide SPC System.
9. If you wish to explore large amounts of (typically business or market related) data in search for consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data you may be looking for the popular Data Mining and Data Warehousing concepts.