To check the distribution of a dataset's values, you can utilize various techniques depending on the tools you are familiar with. The first step is to visualize the data using histograms or density plots, which provide a simple way to see how values are spread across different ranges. For instance, in Python, you can use libraries like Matplotlib or Seaborn to create a histogram that shows you the frequency of each value range in your dataset. This visual representation helps you identify patterns, such as whether the data is normally distributed, skewed, or has multiple peaks.
Statistical measures can also provide insights into the distribution. You can compute basic metrics such as mean, median, mode, and range to understand the central tendencies of your dataset. In addition to these, looking at quantiles or percentiles can help you see how data is distributed across the entire range. For example, using NumPy, you can easily obtain the 25th, 50th, and 75th percentiles to gauge the spread of your data. These metrics give you a clearer understanding of how values are clustered or spread apart in your dataset.
Finally, you might want to perform more advanced analysis using statistical tests to assess the goodness-of-fit for a particular distribution. For example, the Shapiro-Wilk test can help determine if your data follows a normal distribution. In Python, you can use SciPy's implementation of this test. Additionally, comparing the empirical cumulative distribution function (CDF) of your dataset with theoretical distributions can provide a visual and analytical summary of how well your data aligns with expected patterns. By combining visualization, descriptive statistics, and statistical tests, you can gain a comprehensive view of your dataset's distribution.