Abc of Research Methodology and Applied Biostatistics by M.N. Parikh;Nithya Gogtay
Author:M.N. Parikh;Nithya Gogtay [M.N. Parikh;Nithya Gogtay]
Language: eng
Format: epub
Published: 2012-10-12T07:14:00+00:00
Fig. 5.2: Normal distribution showing bell curve and Standard Deviation (SD)
Fig. 5.3: Flatter bell curve of normal distribution
Fig. 5.4: Steep bell curve of normal distribution
STANDARD DEVIATION, VARIANCE AND STANDARD ERROR OF MEAN
Standard deviation: This is a measure of dispersion or variability in the data and was formulated by Galton in the late 1860s. Often, we want to know not just the mean, but also how far away values are from the mean. This is given by SD. When the data points are fairly close to the mean, the SD will be narrow and the bell curve will be steep. When the data points are spread out, the SD will be wide and the bell curve will be flatter.
Variance: Variance which is also the square of the standard deviation is defined as "the average of the squared differences from the mean". Like the standard deviation, it also is a measure of the variability or spread of the data. It is calculated by subtracting individual values from the mean, squaring them to remove negative signs, adding them together and then averaging them. When the square root of this value is taken, it gives the standard deviation.
Standard error ofinean: Our ultimate aim in statistics is to generalize or extrapolate to the population the research findings. We do this by drawing samples, calculating mean and SD and then extrapolating the data. When multiple samples are drawn from the same population, each sample will have its own mean and own standard deviation. All of these will be a little close or a little away from the true population mean. When all these diverse means are plotted together, it can be shown that they also follow a normal distribution and have their own SD. The SD of a population of means (i.e. SD of a number of means) is called the standard error of mean or SEM, which is in fact a misnomer.
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