But where there is interaction between the chosen variables, especially where the humans are involved as a variable unlike inanimate objects like gases or salt will not produce the same linear results that could be expected from a scientific experiment as in physics for example. In contrast, the interaction between the multifarious individuals that comprise of the data collected may actually delay or change the patterns of the results based on many factors "that actually may dampen the individual effects of the two variables, as when two noises combine to create a zone of apparent quiet. Two gases may be relatively harmless when released into the atmosphere separately, but may yield lethal toxins when released together." When we test the interaction effects in the case of the managers and customers of the bank, and try to establish the level of the customer relationship management, there is the same dilemma. Statistics can be used at best when the researcher has designed the experiment properly. For example where human feelings and interactions are involved, individual responses that are prompted by extraneous factors like personal feelings, the different view, or definition of relation and service that an individual may have, it is pertinent at this juncture to see if modern analysis methods that have evolved specifically for business analysis will fit the case. The importance of the method and the tools can be seen if we analyze a real experiment. For example if we were to study the fact that cat owners tend to get diseases from their pets, there are many variables that have to be considered. For example does the owner fondle his cat? What are the precautions that the cat owner takes to keep off from being infected? Here individuals participating vary and are unpredictable variables. Statistics has tools that are effective even in such dilemma. The method used is based on the experimental design.

Experimental Design

In this hypothetical experiment the cats and their owners have to participate and as a control group the individuals who have no feline pets are used. The experiment is designed keeping in mind that in scientific experiments involving humans, the experiment becomes a well planned observational process by which a question can be answered to certainty or an understanding can be reached of the external world. This is done through the observation-hypothesis-experiment. It begins with a chance observation of a new phenomenon.

The important part in the design is finding the appropriate variables. Therefore the experiment has two sets of participants -- one being the households that have cats, and another set in equal number that do not own or have cats. It boils down to a single variable if the family has a cat or not. This is the use of a single variable but not suited to this purpose although the primary position is that it is very easy to summarize results in the case of a single variable. Normally a research cannot be done in the boundary of a single variable but rather the interconnectedness of the variables is the subject of the study. Thus two variables if proved are related, could help in using the information about one to predict the other. Thus in this case the two variable models where the use of one variable is used to predict the probability of the outcome of the other is the bivariate regression model.

Another test that has been considered is the chi-square (x2) distribution which is by far the best for data analyses, and can be used to determine if the variables are dependent or independent. These considerations have prompted the following model for this research: In this case it is to be remembered that there are many pitfalls and things that would not be considered and these may lead to errors. Statistics has no answer to inherent error correction methods if the design is faulty. However statistical methods do have inherent error correction facility. For example in the analysis of the hypothetical cat disease, the discussion can go beyond the suspected diseases that the cat can pass on to any disease that is not yet suspected. This can throw more light on the issue. For example itching if noticed with cat owners but not so with the control group can be a positive indication that the itch may be caused by some dealing with the cat.

Then once this is established there can be further investigation into the issue as a sub-research. There are thus very few variables and the outcome will be based on the explanatory variables used to test the main hypotheses and this must be precise measurements that can be used to accurately measure the outcome, and also later measure the impact of the interventions...

In order to design the experiment the major considerations that were taken was the fact that the design can validate the data, that the construct of the experiment is valid and that this experiment can be repeated.
Error creeps in at the sampling time. One of the important tools of statistical method for collecting data is the survey. Survey is relied as the best instrument of data collection. In simple terms the survey "is not just a particular technique of collecting information: questionnaires are widely used but other techniques, such as structured and in-depth interviews, observation, content analysis and so forth, can also be used in survey research."

The possibility of error is ever present. Experimental error of the general nature is any error that could be found in an experiment. These errors, like the constant errors and the random error have to be watched and guarded against. Random errors are likely to occur in sampling and errors could occur in the design of the experiments.

When there are extraneous variables, the experimental error and its possibility which affect the validity are critical. Validity is jeopardized when there are extraneous variables, or a conceptual error in the experiment, which we could call experimental error. The best means of identifying if the experiment was error free and its validity established is by looking closely at the data that collected. The data collected is the base of the result and hence if the data did not represent the actual field condition or the validity of the sample collected in terms of its conformity with the needs of the experiment could not be established the validity of the outcome will not be accurate.

That is the use of statistics in the research activities. However it is not confined only to the academics. However the use of statistics can be seen in the everyday lives of the people.

Common uses in every day life

The use of statistics can be seen in marketing; advertising and the process of planning customer related research and campaigns. It can also be seen in the gauging of the popularity of new products. The box office is nothing but the statistics of the popularity of a movie. More business applications can be found in the financial and stock markets where many statistical tools like correlation and averages are used to determine values. For example to determine the relationships between stock prices and volumes of sales are examined with the view that they are joint products of a single market mechanism. The results found here tend to support the notion that any model of the stock market which separates prices from volumes or vice versa will inevitably yield incomplete if not erroneous results.

Conclusion

Statistics can be defined as a study of variability and enumeration. There are many tools for statistical analysis. Statistics is used in all scientific studies -- physics, medicine and extends to management, finance and the market analysis. Statistical methods can be used to design and analyze the results of the experiments making them very reliable and error free. Statistics form the basis of all empirical research. It involves the process of collecting and analyzing data. It is pertinent to note that the study of. Statistics by itself is a science that can be used for quantitative and qualitative data analysis and researchers now days are using 'multivariate analysis' especially the 'partial correlation.' To make the experiment successful the researcher ought to have designed the experiment properly and avoid the pitfalls related to inherent error. The error could creep in from the basic tool- the survey or observation section. Random errors thus may occur during sampling and in the design of the experiments. Today statistics is being used as an information tool in almost all walks of human activity.

References

Baird, D.C. (1962) "Experimentation: An Introduction to Measurement Theory and Experiment Design." Prentice-Hall: Englewood Cliffs, NJ.

Ezekiel, Mordecai; Fox, Karl a. (1959) "Methods of Correlation and Regression Analysis,

Linear and Curvilinear" John Wiley & Sons: New York.

Kault, David. (2003) "Statistics with Common Sense."

Greenwood Press: Westport, CT.

Marcoulides, George a; Schumacker, Randall E. (1996) "Advanced Structural Equation

Modeling: Issues and Techniques." L. Erlbaum Associates: Mahwah, NJ.

Myers, Jerome L; Well, Arnold…