Williams sorts through the mix of information, audience and channel, carving out concise, coherent content from raw intel to help clients overcome communications challenges. Skip to main content. Focusing on Big Picture Statistical analysis of a representative group of consumers can provide a reasonably accurate, cost-effective snapshot of the market with faster and cheaper statistics than attempting a census of very single customer a company may ever deal with.
Backing Judgments Statistics back up assertions. Making Connections Statistics can point out relationships. Ensuring Quality Anyone who has looked into continuous improvement or quality assurance programs, such as Six Sigma or Lean Manufacturing, understands the necessity for statistics. References 2 VCube Solutions: Resources 1 University of Baltimore: Statistical Thinking for Managerial Decisions. About the Author Building on two decades of experience in market research and business writing, John T.
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A critical region is the set of values of the estimator that leads to refuting the null hypothesis. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false. Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms.
For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug is unlikely to help the patient noticeably. Although in principle the acceptable level of statistical significance may be subject to debate, the p-value is the smallest significance level that allows the test to reject the null hypothesis.
This test is logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the p-value, the lower the probability of committing type I error.
Some problems are usually associated with this framework See criticism of hypothesis testing:. Some well-known statistical tests and procedures are:. Misuse of statistics can produce subtle, but serious errors in description and interpretation—subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors.
For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics. Even when statistical techniques are correctly applied, the results can be difficult to interpret for those lacking expertise. The statistical significance of a trend in the data—which measures the extent to which a trend could be caused by random variation in the sample—may or may not agree with an intuitive sense of its significance.
The set of basic statistical skills and skepticism that people need to deal with information in their everyday lives properly is referred to as statistical literacy. There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter.
Misuse of statistics can be both inadvertent and intentional, and the book How to Lie with Statistics  outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted e. Warne, Lazo, Ramos, and Ritter Ways to avoid misuse of statistics include using proper diagrams and avoiding bias. Thus, people may often believe that something is true even if it is not well represented.
To assist in the understanding of statistics Huff proposed a series of questions to be asked in each case: The concept of correlation is particularly noteworthy for the potential confusion it can cause.
Statistical analysis of a data set often reveals that two variables properties of the population under consideration tend to vary together, as if they were connected.
For example, a study of annual income that also looks at age of death might find that poor people tend to have shorter lives than affluent people. The two variables are said to be correlated; however, they may or may not be the cause of one another. The correlation phenomena could be caused by a third, previously unconsidered phenomenon, called a lurking variable or confounding variable. For this reason, there is no way to immediately infer the existence of a causal relationship between the two variables.
See Correlation does not imply causation. Some scholars pinpoint the origin of statistics to , with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences. Its mathematical foundations were laid in the 17th century with the development of the probability theory by Gerolamo Cardano , Blaise Pascal and Pierre de Fermat.
Mathematical probability theory arose from the study of games of chance, although the concept of probability was already examined in medieval law and by philosophers such as Juan Caramuel. The modern field of statistics emerged in the late 19th and early 20th century in three stages. Ronald Fisher coined the term null hypothesis during the Lady tasting tea experiment, which "is never proved or established, but is possibly disproved, in the course of experimentation".
The second wave of the s and 20s was initiated by William Gosset , and reached its culmination in the insights of Ronald Fisher , who wrote the textbooks that were to define the academic discipline in universities around the world. Edwards has remarked that it is "probably the most celebrated argument in evolutionary biology ". The final wave, which mainly saw the refinement and expansion of earlier developments, emerged from the collaborative work between Egon Pearson and Jerzy Neyman in the s.
They introduced the concepts of " Type II " error, power of a test and confidence intervals. Jerzy Neyman in showed that stratified random sampling was in general a better method of estimation than purposive quota sampling. Today, statistical methods are applied in all fields that involve decision making, for making accurate inferences from a collated body of data and for making decisions in the face of uncertainty based on statistical methodology.
The use of modern computers has expedited large-scale statistical computations, and has also made possible new methods that are impractical to perform manually. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data. Applied statistics comprises descriptive statistics and the application of inferential statistics. Mathematical statistics includes not only the manipulation of probability distributions necessary for deriving results related to methods of estimation and inference, but also various aspects of computational statistics and the design of experiments.
There are two applications for machine learning and data mining: Statistics tools are necessary for the data analysis. Statistics is applicable to a wide variety of academic disciplines , including natural and social sciences , government, and business. The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science. Early statistical models were almost always from the class of linear models , but powerful computers, coupled with suitable numerical algorithms , caused an increased interest in nonlinear models such as neural networks as well as the creation of new types, such as generalized linear models and multilevel models.
Increased computing power has also led to the growing popularity of computationally intensive methods based on resampling , such as permutation tests and the bootstrap , while techniques such as Gibbs sampling have made use of Bayesian models more feasible.
The computer revolution has implications for the future of statistics with new emphasis on "experimental" and "empirical" statistics.
A large number of both general and special purpose statistical software are now available. Traditionally, statistics was concerned with drawing inferences using a semi-standardized methodology that was "required learning" in most sciences.
What was once considered a dry subject, taken in many fields as a degree-requirement, is now viewed enthusiastically. Statistical techniques are used in a wide range of types of scientific and social research, including: Some fields of inquiry use applied statistics so extensively that they have specialized terminology.
In addition, there are particular types of statistical analysis that have also developed their own specialised terminology and methodology:. Statistics form a key basis tool in business and manufacturing as well. It is used to understand measurement systems variability, control processes as in statistical process control or SPC , for summarizing data, and to make data-driven decisions.
In these roles, it is a key tool, and perhaps the only reliable tool. From Wikipedia, the free encyclopedia. For other uses, see Statistics disambiguation. Statistical data type and Levels of measurement. History of statistics and Founders of statistics. List of fields of application of statistics.
Actuarial science assesses risk in the insurance and finance industries Applied information economics Astrostatistics statistical evaluation of astronomical data Biostatistics Business statistics Chemometrics for analysis of data from chemistry Data mining applying statistics and pattern recognition to discover knowledge from data Data science Demography statistical study of populations Econometrics statistical analysis of economic data Energy statistics Engineering statistics Epidemiology statistical analysis of disease Geography and geographic information systems , specifically in spatial analysis Image processing Medical statistics Political science Psychological statistics Reliability engineering Social statistics Statistical mechanics.
Abundance estimation Data science Glossary of probability and statistics List of academic statistical associations List of important publications in statistics List of national and international statistical services List of statistical packages software List of statistics articles List of university statistical consulting centers Notation in probability and statistics.
Foundations of statistics List of statisticians Official statistics Multivariate analysis of variance. Stanford Encyclopedia of Philosophy. Meaning and Definition of Statistics". Statistics for the Twenty-First Century. The Mathematical Association of America. Handbook of stochastic analysis and applications. Theory of statistics Corr. A New Kind of Science.
Theory and Practice , Cambridge University Press. Modern Epidemiology 3rd ed. Data analysis and regression. The knowledge needed to computerise the analysis and interpretation of statistical information.
In Expert systems and artificial intelligence: Library Association Report, London, March, 23— Cartography and Geographic Information Science. Choosing an analysis method.
Measurement theory and practice:
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