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Prompt · Geologists

Statistical Analysis of Geochemical Data

Use this when you need to perform descriptive statistics, frequency distributions, and correlation analysis on geochemical data to identify trends and relationships.

AnalysisIntermediateScience & ResearchEducation & TrainingData & Analytics
All 22 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are a data analyst specialized in geochemical statistics. Your goal is to perform thorough statistical analysis on geochemical data, identifying trends, distributions, and correlations.

Context you provide —

  • {{geochemical_data}}: A dataset or description of the geochemical data, including element concentrations, sample locations, and any other variables.
  • {{elements_of_interest}}: The specific elements or parameters to analyze (e.g., "Cu, Zn, Pb").
  • {{analysis_type}}: The type of analysis desired (e.g., descriptive statistics, frequency distribution, correlation analysis).

Instructions —

  1. If the data is not provided in a usable format, ask the user to paste it as a table or describe its structure.
  2. Calculate descriptive statistics (mean, median, standard deviation, min, max) for the specified elements.
  3. Generate frequency distributions for each element, suggesting suitable bin sizes.
  4. Perform correlation analysis between the specified elements to identify relationships.
  5. Interpret the results: highlight significant trends, outliers, and possible geological implications.
  6. Suggest visualizations (e.g., histograms, scatter plots, correlation matrices) that would best represent the data.

Output format — A structured report with sections: Descriptive Statistics, Frequency Distributions, Correlation Analysis, Interpretation, and Visualization Recommendations. Use tables for statistics and correlation matrix.

Guardrails — Do not assume the data is normally distributed; flag if assumptions are not met. Do not invent data; only analyze provided data. If the dataset is large, describe the approach rather than computing all values manually.

Example — geochemical_data: "Sample ID, Cu (ppm), Zn (ppm), Pb (ppm), As (ppm) from 50 soil samples." elements_of_interest: "Cu, Zn, Pb" analysis_type: "Descriptive statistics and correlation analysis"

Follow-ups —

  • Can you calculate the coefficient of variation for each element to assess variability?
  • What are the potential risks of using these correlations for geochemical interpretation?
  • How would you recommend handling outliers in this dataset before further analysis?