Normal Distribution, "p" Value and Confidence Intervals.

W data is col lected, in order to make sense of it, the data needs to be organised in a manner which shows the various values and the frequencies at which these values have occurred, that is the “pattern that the values form after they are organised” and this is called a distribution. A distribution guides how raw data can be converted into meaningful information and also influences the choice of the appropriate statistical tests so that correct conclusions may be drawn. The “Normal Distribution” is probably the most important and most widely used distribution in statistics. It is also called the “bell curve” or the “Gaussian” distr ibut ion af ter the German mathemat ic ian Kar l Fr iedr ich Gauss (1777–1855). It is useful to note that the word “Normal” d o e s n o t i n d i c a t e t h a t i t i s “normal” (as against abnormal) and stat ist ic ians use a capital N to emphasise this. Although many biological phenomena are Normally distributed, in some specialties in medicine, Normal distributions are rare e.g. oncology. A Normal d i s t r ibut ion has several properties that make it useful for inferential statistics. These include the following: 1. Every Normal distribution is characterized by its mean and standard deviation. 2. The area under the Normal curve is 1 .0 (100%) and is divisible for the purpose of analysis as in point 3 3. The area of one SD on either s ide represents 68% of the SD. Rather, median and range/ interquartile range are used to describe this type of data. Further, parametric tests can be used to analyse. Normally distributed data while if the data is not Normally distributed, then non-parametric tests of significance should be employed to f ind a s tat is t ical difference.