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A histogram is a graphical representation of the distribution of numerical data, where the data is divided into intervals (bins) and the frequency of data points within each interval is represented by the height of bars. It helps visualize the shape of the data distribution, making it easier to identify patterns such as central tendency, variability, and skewness.

Skewness refers to the asymmetry of the distribution of data values in a dataset. A distribution is considered positively skewed (right-skewed) if it has a longer tail on the right side, while a negatively skewed (left-skewed) distribution has a longer tail on the left side. Understanding skewness is crucial for interpreting the shape of the histogram and the implications for data analysis.

A frequency distribution is a summary of how often each value occurs in a dataset. It organizes data into categories or intervals, showing the number of observations (frequency) for each category. This foundational concept is essential for constructing histograms, as it provides the necessary data to visualize the distribution and assess characteristics like skewness.