How do you test the validity and reliability of your scatter plots and heatmaps?

Directly plotting raw data can be beneficial but should be done with caution. Best practice before visualization or descriptive statistics first check your data quality: Data Completeness: Are there any missing values? Data Accuracy: Have you checked the data for e.g. outliners? Data entry errors? Data Consistency: Habe you checked for internal inconsistencies within the data? E.g. negative age ? Data Uniqueness: Any duplicates?

Visualization clearly depends on the data you want to show. Here are some common Plot Types: Histograms: Visualizing the distribution of a single numerical variable. Helps to identify patterns and outliers. Box plots: Effective for comparing distributions across different groups or categories. Scatter plots: Useful for exploring relationships between two numerical variables. For analysis or correlations, clusters, and outliers. Bar charts: For comparing categorical data across different categories. Line charts: Effective for visualizing trends over time or across continuous variables. Density plots: Similar to histograms, often used for visualizing probability density functions.

While correlation coefficients (Pearson's r, Spearman's rho) are essential for quantifying linear or monotonic relationships, it's crucial to consider also other factors: Causation: Correlation does not imply causation. Just because two variables are correlated doesn't mean one causes the other. Outliers: Extreme values can significantly impact correlation coefficients. Non-linear Relationships: Correlation measures linear relationships. If the relationship is non-linear, consider other methods like polynomial regression. Third-variable Problem: A third variable might be influencing both variables. Data Distribution: Normality assumptions for Pearson's r. If data is not normally distributed, Spearman's rho might be more appropriate.

Since hypothesis tests can be too limited, you might also consider: Effect Size: Magnitude of the relationship. Determines if a statistically significant effect is practically meaningful, or not. Correction for multiple comparisons: When conducting multiple hypothesis tests, adjust p-values to account for increased Type I error rate. Methods: Bonferroni correction, False Discovery Rate (FDR) control.

Check the data: Before creating any visualization, you need to ensure that your data is accurate, complete, and consistent. Make sure there are no missing values, outliers, or errors that could affect your plot. Test for correlation: If you're creating a scatter plot, you can test for the strength and direction of the correlation between two variables using statistical methods such as Pearson correlation coefficient or Spearman rank correlation. A strong correlation indicates that the data points are closely clustered around a line, while a weak correlation suggests that the data points are more spread out.

Use multiple visualizations: To improve the reliability of your results, it's a good idea to use multiple visualizations to represent the same data. For example, you could create a scatter plot, a heat map, and a regression analysis. If all the visualizations show the same patterns and trends, then you can be more confident that your results are valid and reliable. Test with different subsets: You can also test the validity and reliability of your plots by creating them with different subsets of your data. This can help you identify any biases or anomalies in your data and ensure that your plots are representative of the overall dataset.

While comparing scatter plots and heatmaps is a good starting point, you can do also following to enhance understanding: Overlaying Plots: Combine different plot types to reveal underlying patterns. For instance, overlay a density plot on a scatter plot to visualize data distribution and clusters. Faceting: Splitting data into subsets based on categorical variables and creating separate plots for each subset. This helps identify patterns within subgroups.