Bootstrap Approach in Data Sampling

As shown in the previous chapter, the basic samples of data needed to calculate the confidence intervals have distributions which depart from the traditional parametric distributions. Thus, classical hypothesis-testing procedures based on strong parametric assumptions cannot be used to estimate the confidence intervals. In order to obtain results as reliable as possible, a statistical technique which is applicable regardless of the form of the data probability density function has to be utilized. In other words, this method should make no assumption about the different data distributions. One good candidate is the bootstrap method.

The idea about bootstrapping is that we don’t have enough data. To illustrate this technique, we consider a clothing shop selling second clothing. In a week, sales per day varies with day. The second week analysis also, shows a different trend to first week. As a business owner, one would like to determine the customer behaviour and pattern so that planning can be done effectively to ensure customer satisfaction. Therefore, an average or mean sales need to be determine for the first week. This is done by taking resample data from the main data sample, which will produce a distribution sample as indicated in (a) and (b) for week 2. The t-test statistical analysis will not be the reasonable method to determine the resalable mean value since it will recognise both (a) and (b) as normal distribution, thus giving a

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