# Statistical Techniques

In an age where information has taken a critical role in nearly all areas of human existence, the role played by statistical techniques cannot be over emphasized.  Statistics as a field of study is concerned with the science of collecting, analyzing, interpreting and presenting data.  Irrespective of the statistical model or technique one uses in the research process, it is important that the approach addresses the objectives of the research. Statistical analysis in research is robust as it provides approaches for even making forecasts based on the data and determining error margins.

Statistical techniques find use in nearly all academic disciples including business studies, social sciences, natural sciences and even humanities. The prevalent use of statistical techniques in nearly all areas of study is resultant of the critical role played by research in all areas of human orientation.

Descriptive statistics is generally used in the summarization of data.  It is worth noting that irrespective of the statistical approach used data must be collected (Taylor, & Cihon, 2004).  Moreover, the variables of concern have to be quantified if a statistical approach is to be used in analyzing a phenomenon.

Summarization of data which gives a depiction of a population from a single parameter or

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statistic makes it easy to make inferences on a population.  In most cases it is not easy to observe trends in a population or access all members of a population; descriptive statistics come in handy in summarizing parameters on the population and developing inferences on the population that aids achievement of research objectives.

An important area in statistical technique is to always ensure randomness of the variables being analyzed.  Nearly all statistical approaches are based on the assumption that the variables being analyzed are random thus the collection and analysis of data is done in a manner that ensures randomness.

Lottery method and random number generators are some of the techniques used in ensuring randomness especially in sampling that is common in survey approaches to statistical analysis (Taylor, & Cihon, 2004).  Though data can be collected in an observational or experimental setting, statistical analysis will always serve to meet the objectives of describing data and making inferences regarding the population being studied.

The mean and standard deviation are some of the statistics used in seeking the descriptive goals in statistical analysis to determine the levels of dispersion and central tendency.  The use of graphical summarizations and charts come in handy in presenting findings that are descriptive in nature (Taylor, & Cihon, 2004).  An important application of the sample mean and standard deviation is in estimating the value of the population mean and variance as is the case in the survey and parameter estimation.

Having a clear picture of what the data collected represents is not enough.  Modeling of data, developing an account for randomness and making inferences on the population are important in understanding the nature of the population with regards to research objectives.  Hypothesis testing, estimation, correlation, regression, time series and ANOVA are some of the approaches that find use in determining more complex trends from data (Taylor, & Cihon, 2004).  Choice of any of the approaches should be guided by the objectives that a researcher is seeking.

Statistical techniques may take several years to master for they are complex and have clearly defined approaches that they employ. Development of statistical packages like SAS and SPSS has however made it easy for researcher to analyze data without having the intricate details of the approaches that they are employing (Taylor, & Cihon, 2004).  This may be a development in the positive direction though no package or program can replace the importance placed on choosing the best approach to statistical analysis that must be guided by the objectives a researcher is seeking.

References

Taylor, J.K., & Cihon, C. (2004). Statistical Techniques for Data Analysis: John K. Taylor           and Cheryl Cihon. (2nd ed.). Boca Raton, FL: CRC Press

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