Though this universe is full of uncertainty and variability, a large set of experimental/biological observations always tend towards a normal distribution. This unique behavior of data is the key to entire inferential statistics.

**Types of
Distribution:**

Though this universe is full of
uncertainty and variability, a large set of experimental/biological
observations always tend towards a normal distribution. This unique behavior of
data is the key to entire inferential statistics. There are two types of
distribution.

**Gaussian /normal distribution: **If
data is symmetricallydistributed on both sides of mean and form a bell-shaped
curve in frequency distribution plot, the distribution of data is called normal
or Gaussian. The noted statistician professor Gauss developed this, and
therefore, it was named after him. The normal curve describes the ideal
distribution of continuous values i.e. heart rate, blood sugar level and Hb %
level. Whether our data is normally distributed or not, can be checked by putting
our raw data of study directly into computer software and applying distribution
test. Statistical treatment of data can generate a number of useful
measurements, the most important of which are mean and standard deviation of
mean. In an ideal Gaussian distribution, the values lying between the points 1
SD below and 1 SD above the mean value (i.e. ± 1 SD) will include 68.27% of all
values. The range, mean ± 2 SD includes approximately 95% of values distributed
about this mean, excluding 2.5% above and 2.5% below the range [Fig. 2]. **In ideal distribution of thevalues: **the
mean, mode, and median are equal withinpopulation under study. Even if
distribution in original population is far from normal, the distribution of
sample averages tend to become normal as size of sample increases. This is the
single most important reason for the curve of normal distribution. Various
methods of analysis are available to make assumptions about normality,
including‘t’ test and analysis of variance (ANOVA). In normal distribution,
skew is zero. If the difference (mean–median) is positive, the curve is
positively skewed and if it is (mean–median) negative, the curve is negatively
skewed, and therefore, measure of central tendency differs [Fig. 2].

** Non-Gaussian (non-normal) distribution: **If
the data is skewedon one side, then the distribution is non-normal. It may be
binominal distribution or Poisson distribution. In binominal distribution,
event can have only one of two possible outcomes such as yes/no,
positive/negative, survival/death, and smokers/non-smokers. When distribution
of data is non-Gaussian, different test like Wilcoxon, Mann-Whitney,
Kruskal-Wallis, and Friedman test can be applied depending on nature of data.

**Standard Error of Mean**

Since
we study some points or events (sample) to draw conclusions about all patients
or population and use the samplemean (M) as an estimate of the population mean
(M1), we need to know how far M can vary from M1 if repeated samples of size N
are taken. A measure of this variability is provided by Standard error of mean
(SEM), which is calculated as (SEM = SD/√n). SEM is always less than SD. What
SD is to the sample, the SEM is to the population mean.

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

**Related Topics **

Copyright © 2018-2020 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.