This is a well- organized and summarized tabulation or graphical representation of each individual data in every category on a scale of measurement.
Uses of a Frequency Distribution
Briefly identify the differences between a normal, positive and negative skew
- It enables the medical researcher to have a glance at the entire data presisely and conveniently.
- It can show whether the observations are low or high and if they are concentrated in one area or spread all over the entire scale i.e. if they are positively skewed.
- It gives a picture to the researcher of how the individual observations are distributed in the measurement scale and therefore he/she can make a decision on the way forward.
Normal curve- The normal curve is a bell-shaped symmetrical curve with the peak of the distribution at the center and the ‘tails’ of the distribution continually approaching, but never touching the horizontal axis. This kind of curve is a mathematical concept, which is not realized by any real data, but plays an important role in statistical inference such as in Biostatistics.
Another fundamental characteristic of a normal curve are the the three measures of central tendency are located exactly as the center.
Positively skewed distribution- in this kind of distribution, the measures tend to pile up at the lower end of the scale and trail of in terms of the frequency toward the upper end. Another important information is that here the mean is greater than the median and the median is greater than the mode. For example, this may imply that those children who were vaccinated against polio many of them showed good response to the vaccine.
Negatively skewed distribution- here the distribution trail of towards the lower end. Here the tail of the distribution lies in the negative scale of the distribution. Here again the mode is greater than the median and the median is greater than the mean this explains how many patients reacted positively to the drug while a few showed negative response to the drug.
Relationship between the standard error and skews around a distribution
The Standard Error of Skewness enables one t tell the deviation that can exist between the values of Skewness in multiple samples that will be taken randomly from the same of the distributionpopulation distribution as the sample of analysis. A zero value shows that the deviation of values of skewness between multiple samples is zero and thus, the underlying distribution of the current sample also does not deviate from a symmetric distribution. For instance, in that case, the current sample can be said that has a symmetric distribution, too. It should be noted that higher values show higher deviation of the underlying distribution of the sample from a symmetric distribution.
The Skewness in medical statistics tells us about some properties of the dispersion of a dataset and thus the shape as a Graph Figure of this dataset. When the size of a dataset is small, the sample skewness statistics cannot be representative of the true skewness in the reference population. Hence, the Standard Error of Skewness helps here. These standard errors are able to show the deviation that can exist between the values of Skewness in multiple samples which are taken randomly from the same underlying population distribution as the sample of analysis was taken.
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