To determine the distribution of a sample, you can use various statistical methods such as histogram, boxplot, or normality tests.
One way to identify the distribution of a sample is by creating a histogram. A histogram is a graphical representation of the data where the x-axis represents the range of values and the y-axis represents the frequency or count of those values. By plotting the sample data on a histogram, you can visually see the shape of the distribution. For example, if the histogram forms a bell-shaped curve, the sample may have a normal distribution.
Another method is to create a boxplot, also known as a box-and-whisker plot. This graphical representation shows the minimum, first quartile, median, third quartile, and maximum of the data. By examining the lengths of the boxes, whiskers, and the presence of outliers, you can gain insights into the distribution. For instance, if the data is symmetrically distributed around the median and the box is approximately centered between the whiskers, it suggests a symmetrical distribution.
Additionally, you can employ normality tests to determine if a sample follows a normal distribution. These tests, such as the Shapiro-Wilk test or the Anderson-Darling test, provide statistical evidence of normality. If the null hypothesis of these tests is not rejected (p-value > 0.05), it suggests that the sample may follow a normal distribution.
It is important to note that these methods can help provide insights into the distribution, but they do not guarantee the true distribution of the population. They are tools for analyzing the data and understanding the characteristics of the sample.
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