Statistics Standard Deviation

Standard deviation is the square root of the variance (Gravetter & Wallnau, 2013). It uses the mean of the distribution as a reference point and measures variability by considering the distance of each score from the mean. It is important to know the standard deviation for a given sample because it gives a measure of the standard, or average, range from the mean, and specifies if the scores are grouped closely around the mean or are widely scattered (Gravetter & Wallnau, 2013). The standard deviation provides researchers with information on the score standardization about the mean. This is important because, in a normal distribution, scores are symmetrically distributed with the highest frequency at the midpoint, and lower occurrences spread
The null hypothesis, represented by H0, states that the population parameter is equal to the stated value (Gravetter & Wallnau, 2013). The null hypothesis estimate is based entirely on chance. Thus, the null hypothesis is true if the corrected data do not differ from what would be expected from chance alone. The alternative hypothesis is represented by H1 and is a complement of the null hypothesis; that is, the finding did not occur by chance. The alternative hypothesis is a statement of what the research is set to establish. Hypothesis test assesses two mutually separate statements about a population to determine which statement is best supported by the sample data; the null hypothesis (Ho) no change and the alternative hypothesis (Ha) with change. The null hypothesis is the most important because the sample usually only contains a representative of the data in the population and the researcher cannot be entirely certain if the null hypothesis is true or not. The researcher can only speak about rejecting or not rejecting the null hypothesis based on some test, but not of accepting either of the