Resampling is the method that consists of drawing repeated samples from the original data samples. The method of Resampling is a nonparametric method of statistical inference. In other words, the method of Resampling does not involve the utilization of the generic distribution tables (for example, normal distribution tables) in order to compute approximate p probability values. Resampling involves the selection of randomized cases with replacement from the original data sample in such a manner that each number of the sample drawn has a number of cases that are similar to the original data sample. Due to replacement, the drawn number of samples that are used by the method of Resampling consists of repetitive cases.
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Resampling is also known as Bootstrapping or Monte Carlo Estimation. Resampling generates a unique sampling distribution on the basis of the actual data. The method of Resampling uses experimental methods, rather than analytical methods, to generate the unique sampling distribution. The method of Resampling yields unbiased estimates as the method of Resampling is based on the unbiased samples of all the possible results of the data studied by the researcher.
In order to understand the concept of Resampling, the researcher should understand the terms Bootstrapping and Monte Caro estimation.
The method of bootstrapping, which is equivalent to the method of Resampling, utilizes repeated samples from the original data sample in order to calculate the test statistic.
Monte Carlo estimation, which is also equivalent to the bootstrapping method, is used by the researcher to obtain the Resampling results.
There are certain assumptions that are made by the researcher while conducting the method of Resampling.
This method of Resampling is generally based on nonparametric assumptions.
This method of Resampling generally ignores the parametric assumptions that are about ignoring the nature of the underlying data distribution. Therefore, Resampling is based on nonparametric assumptions.
Sample size assumption of the Resampling: In Resampling, there is no specific sample size requirement. Therefore, the larger the sample, the more reliable the confidence intervals generated by the method of Resampling.
In the method of Resampling, there is an increased danger of over fitting noise in the data. This type of problem can be solved easily by combining the method of Resampling with the process of cross-validation.
In SPSS, the researcher can perform the method of Resampling in the following manner:
After selecting “Nonparametric Tests” from the analyze menu, the researcher clicks on “Two Independent Sample tests,” where the researcher finds an "Exact" button. This button in SPSS is used to conduct the process of Resampling, and allows the researcher to make a choice between the types of significance estimates. One such choice the researcher can make includes the method of "Monte Carlo," which is also a Bootstrapping and Resampling method.
