Basics of Statistical Testing
Chanakya Research has emerged as one of the most successful firms catering high end statistical data analysis support to research students struggling with the data analysis part of their thesis/dissertations. All our statisticians possess a brilliant amount of expertise in using the finest statistical testing tools which can ensure the best results for your research task. We use a holistic approach in order to ensure that the data collected as part of the research assignment is included within the thesis/dissertation in an accurate and valid format. Prior to the usage of a specific statistical tool, we ensure to go through the actual aim behind the research conducted by our client.
One of the questions you are most likely to ask in your analysis is:"How does a variable relate to another variable?". In statistical analysis you answer this question by testing the likelihood of the relationship (or one more extreme) occurring by chance alone, if there really was no difference in the population from which the sample was drawn. This process is known as significance or hypothesis testing as in effect, you are comparing the data you have collected with what you would theoretically expect to happen. There are two main groups of statistical significance tests: non-parametric and parametric. Non-parametric statistics are designed to be used when your data are not normally distributed. Not surprisingly, this most often means they are used with categorical data. In contrast, parametric statistics are used with numerical data.
Testing the probability of a pattern such as a relationship between variables occurring by chance alone is known as significance testing. As part of your research project, you might have collected sample data to examine the relationship between two variables. Once you have entered data into the analysis software, chosen the statistic and clicked on the appropriate icon, an answer will appear as if by magic! With most statistical analysis software this will consist of a test statistic, the degrees of freedom (df) and, based on these, the probability (p-value) of your test result or more extreme occurring by chance alone. If the probability of your test statistic or one or more extreme having occurred by chance alone is very low(usually p< 0.05 or lower), then you have a statistically significant relationship. Statisticians refer to this as rejecting the null hypothesis and accepting the hypothesis,often abbreviating the terms null hypothesis to Ho and Hypothesis to H1. Consequently, rejecting a null hypothesis will mean rejecting a testable statement something like 'there is no significant difference between...' and accepting a testable statement something like 'there is a significant between. . ..'. If the probability of obtaining the test statistic or one more extreme by chance alone is higher than 0.05 then you conclude that the relationship is not statistically significant. Statisticians refer to this as accepting the null hypothesis. There may still be a relationship between the variables under such circumstances, but you cannot make the conclusion with any certainty.
Often descriptive or numerical data will be summarised as a two-way contingency table. The chi square test enables you to find out how likely it is that the two variables are associated. It is based on a comparison of the observed values in the table with what might be expected if the two distributions were entirely independent. The test relies on:
- the categories used in the contingency table being mutually exclusive, so that each observation falls into only one category or class interval;
no more than 25 per cent of the cells in the table having expected values of less than 5. For contingency tables of two rows and two columns, no expected values of less than 10 are preferable.
If the latter assumption is not met, the accepted solution is to combine rows and columns where this produces meaningful data. As a research student, if you too are facing issues with the statistical testing process, then you can
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