Interpretable statistical analyses include specific statistics that can be used to arrive at objective conclusions. To properly interpret a statistical analysis, you must understand the hypotheses and the conclusion of the study. The standard statistical practice is to include a pvalue, which allows readers of the statistical analysis to understand the strength of the data present.

Locate the null hypothesis. This is the "default" hypothesis that states the study fails to show anything interesting. For example, in a study comparing two populations in regard to a certain variable, the null hypothesis is that the populations do not differ. Such a hypothesis is often termed "H0" and may be written as "m1 = m1." Interpret this hypothesis in clear language.

Locate the alternative hypothesis. This hypothesis should be near the null hypothesis. It is usually titled "H1" or "Ha." This hypothesis's meaning is often "the null hypothesis is incorrect." If this is the case, you will see a "not equals" sign in a mathematical relation such as "m1 != m2." Sometimes the meaning is "the null hypothesis is incorrect because it over or underestimates the relationship." If so, the sign will be an inequality, such as "m1 > m2" or "m1 < m2." Interpret this according to the meaning of the variables. For example, if the study is comparing height between genders, a null hypothesis of "m1 > m2" may mean that the height of males is greater than that of females.

Find the conclusion. A statistical analysis will conclude that either the null hypothesis or alternative hypothesis is correct. Locate this statement to see the researcher's deduction. Do not interpret the conclusion as fact until you analyze the pvalue.

Observe the pvalue. A pvalue is always included with the conclusion of a hypothesis test. The pvalue will be a number between 0 and 1. It can be multiplied by 100 to obtain a percentage for easier interpretation. The meaning of the pvalue is, by definition, the probability that the researchers concluded in favor of the alternative hypothesis although the null hypothesis is true. If the pvalue is anything above 0.05, do not interpret the conclusion as trustworthy. If the pvalue is under 0.05, it is likely that the researcher's conclusion is factual.
References
 "Research Methods and Statistics"; Sherri Jackson; 2009
 "Statistics"; Joseph Healey; 2009