A normal distribution that has a mean of 0 and a standard deviation of 1 is termed a standard normal distribution (Gravetter & Wallnau, 2015). The normal distribution is one of the most critical probability distribution in statistics because a lot of constant data in nature and psychology exhibits the bell-shaped curve when collected and graphed (Gravetter & Wallnau, 2015). In addition, the most influential (parametric) statistical tests utilized by psychologists involve information to be normally distributed (Krithikadatta and Valarmathi, 2012). We can regulate the values (raw scores) of a normal distribution by transforming them into z-scores. If the data does not bear a resemblance to a bell curve, researchers may have to utilize a less potent kind of statistical test, known as non-parametric statistics (Krithikadatta and Valarmathi, 2012). I would conclude also that normal distributions become more apparent the finer the level of measurement and the greater the sample from a population. You can also calculate coefficients that tell us about the size of the distribution tails in relation to the lump in the middle of the bell curve.

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