Normal Distribution

Probability and Normal Distribution In the study of statistics, probability is a key concept that illustrates and describes the randomness and likelihood of an event from happening given the number of ways it can happen and the number of outcomes that this event could probably happen. That is, probability in statistics helps determine how likely an event is going to happen, as the certainty of the event happening cannot be determined specifically and how many times, but the likelihood of it happening can be determined.

Using probability for the researcher, statistician or business decision-maker would mean going through all possible outcomes for an event or outcome to happen. Randomness and probability makes it possible for an event to not happen at all; or there is an even chance that the event may or may not happen; but there is also a likelihood that the event could happen after all. These are the choices the individual has when s/he is operating under the principle of probability and randomness.

There are different kinds of probabilities: theoretical probability, relative frequency or experimental probability, and personal or subjective probability. Theoretical probability is based on the classical use of a die or coin to determine the likely outcomes of an event from happening and taking note of the actual number of times the event or outcome did occur.

Relative frequency of experimental probability takes into account that the potential number of possible outcomes that an event is going to happen is dependent on the number of trials or times an action was done, and the number of times the expected event or outcome happened. Lastly, personal or subjective probability is the kind of probability that is not calculated but simply, personally expected as a result of the individual's own knowledge and/or feelings about the event and its possible outcomes given specific circumstances.

This probability is not as mathematical as the other kinds of probabilities, but this is also based on the experience of the individual if an event is going to happen or not happen (DePaul University QRC, 2014). A normal distribution is a statistical concept more popularly known as the "bell curve." The normal distribution is best explained by describing its characteristics. A normal distribution is symmetrical, unimodal, and asymptotic.

It is symmetric because in a normal distribution, the upper and lower halves of the distribution are "mirror images of each other." It is also unimodal because there the center of the distribution or top of the bell curve is where the mean, median and mode are also located. Lastly, it is asymptotic because the end tails of the distribution never touch the x-axis. Normal distribution has important implications in statistics, whether it is applied in descriptive statistics or inferential statistics.

In descriptive statistics, the normal distribution is very useful in providing a graphic illustration of the behavior of statistical values and measures. Through the bell curve, the statistician would know the concentration of its high and low values, the spread of the values within a distribution, and whether the values are leaning towards the left or right (skewness). In inferential statistics, the normal distribution is the "baseline" or guide.