Normal and skewed distribution refer to probability theory and statistics. If a probability graph is drawn; a normal distribution means produces a symmetrical graph and a skewed distribution is asymmetrical.
Normal Distribution looks symmetrical in the dispersion with the largest frequencies in the middle scores and tapering down of frequencies towards the highest as well as the lowest scores. One distinct feature of normal distribution is that mostly all the indications of central tendencies as mean, median and mode are the same in value.
For example, if you were to draw a graph showing the probability of rolling each total between two and 12 when two dice are thrown it would make a normal distribution. There would be the same chance of throwing a two as a 12, and seven would represent the peak of the line as it is the most common total.
Skewed Distribution is asymmetrical in dispersion, as skewedness means lopsidedness of frequencies across scores. Distributions can be either negatively skewed or positively skewed.
A normal distribution is symmetrical with the dispersion with the greatest frequency centered in one place towards a central tendency. The mean, mode and the medium are the same value.
A skewed distribution is asymmetrical with dispersion skewed to the left or right of the median. Dispersion skewed to the right is said to be positive with the mean being greater than the mode and median.
Distributions with positive skews are more common than distributions with negative skews. One example is the distribution of income. Most people make under £30,000 a year, but some make quite a bit more with a small number making many millions of dollars per year. The positive tail therefore extends out quite a long way whereas the negative tail stops at zero.
Normal Distribution looks symmetrical in the dispersion with the largest frequencies in the middle scores and tapering down of frequencies towards the highest as well as the lowest scores. One distinct feature of normal distribution is that mostly all the indications of central tendencies as mean, median and mode are the same in value.
For example, if you were to draw a graph showing the probability of rolling each total between two and 12 when two dice are thrown it would make a normal distribution. There would be the same chance of throwing a two as a 12, and seven would represent the peak of the line as it is the most common total.
Skewed Distribution is asymmetrical in dispersion, as skewedness means lopsidedness of frequencies across scores. Distributions can be either negatively skewed or positively skewed.
A normal distribution is symmetrical with the dispersion with the greatest frequency centered in one place towards a central tendency. The mean, mode and the medium are the same value.
A skewed distribution is asymmetrical with dispersion skewed to the left or right of the median. Dispersion skewed to the right is said to be positive with the mean being greater than the mode and median.
Distributions with positive skews are more common than distributions with negative skews. One example is the distribution of income. Most people make under £30,000 a year, but some make quite a bit more with a small number making many millions of dollars per year. The positive tail therefore extends out quite a long way whereas the negative tail stops at zero.
