Theoretic Normal Distribution
Standard deviation often called normal distribution by statisticians is used to see the story behind the data. The data collected often will have high and lows but by seeing the “mean of the mean,” the set of data closest to the average is the data that standard deviation and its concept will have the most meaning to the researcher. The story will emerge and the researcher will have a clearer picture.
Comparison of Theoretic Normal to Actual Data Distribution
The comparisons of theoretic normal distribution of data to actual data distribution are very important property that plays out in certain conditions. The distribution of a large number of independent variables can be approximated as normal. This is that the central limit theorem can be used to approximate normal distributions.
Descriptive statics are used to describe samples of populations by providing a simple summary about the sample and the measures. Together with simple analysis, both form the basis of virtually every quantitative analysis of data. Descriptive statistics are simply describing what the data shows. Descriptive statistics is very different form inferential statistic in that with inferential statistics the researcher tries to infer something about the population using more general condition. Inferential makes judgments about probability with in a study and observed differences within those groups. Unfortunately things of chance do occur in studies. The dependability of inferential statistics might not be as precise as descriptive statistics. When using descriptive statistics we will be able to manage large numbers of measured data in sensible way. Each descriptive statistic reduces lots of data into a simpler summary and provides a powerful summary to compare people and units across many areas of measurements.
Data that does not reflect a normal distribution or is non-normal you must first...