Bins: Equal intervals divide the data range, forming bins. The selection of bin width dramatically influences the histogram's visual effect and should correspond to the data characteristics.

Bars: Bars extend vertically from each bin, their heights corresponding to the frequency or count of observations within the bin. Adjacent placement of the bars signifies the data continuity.

Determine the data range: Identify the dataset's smallest and largest values.

Select the number of bins: Decide on the number of bins needed to divide the data range. Selection criteria may follow different rules, including Sturges' rule, the square root rule, or the Freedman-Diaconis rule.

Create the bins: Partition the data range into equal intervals, according to the selected number of bins.

Count the observations: Ascertain the number of observations falling within the range of each bin.

Draw the bars: Construct a vertical bar for each bin, its height proportional to the count of observations within the bin.

Visualize data distribution: Histograms offer insights into dataset characteristics by identifying patterns, shapes, and central tendencies.

Detect outliers: Histograms facilitate the recognition of data outliers or extreme values, thereby prompting further examination.

Assess normality: They are useful in determining if data adheres to a normal or Gaussian distribution, a prerequisite for many statistical models and tests.

Compare distributions: Histograms provide a visual means to compare different datasets or groups, thereby facilitating an assessment of their attributes.

China's population age distribution varies by year, featuring a significant demographic shift towards an aging population, with increasingly fewer young people and a rising number of elderly citizens.

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