# Frequency Distributions and Sampling

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Frequency Distributions: A normal distribution can be regarded as the most important continuous probability distribution in statistics since it can be utilized to model several sets of measurements in business, industry, and nature. For instance, normal distributions can be used to measure the systolic blood pressure of humans, housing costs, and the lifetime of television sets through random variables. Generally, normal distributions can have any mean and positive standard deviation as the two parameters totally determine the shape of the normal curve during evaluation. In this case, the mean determines the location of the symmetry line while the standard deviation defines how much the data are spread out ("Normal Probability Distributions", n.d.). In the United States, the age at time of death is more likely to closely approximate a normal distribution than annual income because data on income and income inequality are more controversial. Annual income does not closely approximate a normal distribution because there are different approximation methodologies and the fact that households generally have more than a single individual. Furthermore, annual income does not closely approximate a normal distribution because of the continued growth in income inequality due to various factors such as the high rates of unemployment. On the contrary, age at time of death closely approximates a normal distribution because the mortality rates in the United States have a