Prompt lesson · 20 prompts
Statistical Analysis Guidance prompts for Process Development Scientists
20 ready-to-use prompts from our AI for Process Development Scientists course. Copy one, fill in the {{placeholders}}, and paste it into ChatGPT, Claude, Gemini or any other AI.
Apply Non-Parametric Statistical Tests
Use this when you need to select and apply non-parametric tests for data that violates normality assumptions, including preprocessing guidance.
Role You are a statistical consultant specialized in non-parametric methods. Your goal is to help me identify appropriate tests, guide preprocessing, and interpret results for data that does not meet parametric assumptions.
Context you provide
- {{dataset_description}} — brief description of the dataset (e.g., patient blood pressure readings, customer satisfaction scores)
- {{sample_size}} — number of observations
- {{groups_or_variables}} — how the data is structured (e.g., two groups, one group pre/post, multiple groups)
- {{research_question}} — what you want to test (e.g., compare two groups, assess correlation, test for trend)
- {{normality_check}} — whether you have already checked normality and what test indicated failure (e.g., Shapiro‑Wilk p<0.05)
Instructions
- If any context is missing, ask me for it before proceeding.
- Based on the context, recommend 1–3 suitable non-parametric tests (e.g., Mann‑Whitney U, Kruskal‑Wallis, Wilcoxon signed‑rank, Spearman’s rho) and explain why each is appropriate.
- Provide step‑by‑step instructions for running the test, including any necessary data preprocessing (e.g., handling ties, ranking, transformation).
- Interpret the hypothetical output (p‑value, effect size) and explain how to report results in a research paper or presentation.
Output format
- Recommendation section with test names and rationale.
- Step‑by‑step guide with bullet points.
- Interpretation section with example language.
- Tone: educational, precise, accessible.
- Length: 200–300 words.
Guardrails
- Do not assume any software or programming language; keep instructions tool‑agnostic or ask for specific tool.
- Flag any assumptions about the data structure (e.g., independence of observations).
- Do not suggest p‑hacking or data dredging; emphasize pre‑registration of analysis plan.
Example Dataset description: patient blood pressure readings before and after treatment, sample size: 30, groups: paired (pre/post), research question: is there a significant difference? normality check: Shapiro‑Wilk p=0.02
Open this prompt Analysis · Advanced
Build Descriptive Statistics and Visuals
Use this when you need to summarize a dataset with key statistics and visualizations to understand what the data shows.
Role You are a data analyst who turns raw datasets into clear summaries and visuals, optimizing for accurate, decision-ready insights. Context you provide
- {{dataset}}: the dataset or table you want explored (upload or paste).
- {{variables}}: which fields matter most, if known.
- {{audience}}: who will read the results, such as executives or analysts.
- {{goal}}: the key question the statistics should answer.
Instructions
- Ask for the dataset, variables, audience, and goal if any are missing before starting.
- Inspect the dataset and state any data-quality issues such as missing values or outliers that could distort results.
- Compute measures of central tendency and dispersion appropriate to each variable's type.
- Recommend and produce the most useful visualizations, including histograms and box plots for numeric data and bar/pie charts only when they clarify categorical or proportional data.
- Explain what each statistic and visualization reveals in plain language tied to the user's goal.
- Suggest one or two alternative visualizations if they would better suit the audience or message.
Output format Use a concise report with: a short overview, a statistics table, a visualizations section with chart names and rationale, and a 'Key takeaways' list. Keep tone professional and jargon explained. Guardrails
- Do not invent values or visualizations outside the dataset.
- Flag assumptions about missing context instead of silently choosing defaults.
- Stay within descriptive statistics; do not make causal claims.
Example {{dataset}}: customer feedback scores by week; {{variables}}: score and region; {{audience}}: product team; {{goal}}: spot satisfaction trends.
Open this prompt Analysis · Beginner
Conduct Power Analysis for Sample Size
Use this when you need to determine the required sample size or statistical power for an experiment based on your data and research design.
Open this prompt Analysis · Intermediate
Conduct Regression Analysis for Process Optimization
Use this when you need to model relationships between process parameters and outcomes to identify key drivers and predict performance.
Open this prompt Analysis · Intermediate
Data Preprocessing Techniques for Quality Analysis
Use this when you need step-by-step guidance on cleaning, transforming, and encoding your dataset to prepare it for statistical analysis or machine learning.
Open this prompt Learning · Beginner
Design and Interpret Hypothesis Tests
Use this when you need to select, conduct, and interpret hypothesis tests like t-tests and ANOVA for your datasets.
Open this prompt Analysis · Intermediate
Find Statistical Consulting Services
Use this when you need to identify, evaluate, and select statistical consulting firms or experts for a process development project.
Open this prompt Research · Beginner
Guide ANOVA and MANOVA Analysis
Use this when you need step-by-step guidance on conducting ANOVA or MANOVA on your dataset and interpreting results.
Open this prompt Analysis · Intermediate
Interpret Statistical Findings
Use this when you need to interpret statistical findings from your dataset, including key metrics, confidence intervals, and handling outliers.
Open this prompt Analysis · Intermediate
Multivariate Analysis Guidance
Use this when you need guidance on performing multivariate analysis, including correlation analysis, factor analysis, or other techniques to explore relationships among multiple variables.
Open this prompt Analysis · Advanced
Plan A Data Cleaning Approach
Use this when you need a plan for handling missing, inconsistent, or outlier data before analysis.
Open this prompt Analysis · Intermediate
Recommend Statistical Analysis Training Resources
Use this when you need to find courses, books, or workshops to improve your statistical analysis skills specifically for process development.
Open this prompt Research · Beginner
Regression Analysis Guidance
Use this when you need to model relationships between variables, interpret coefficients, and check whether your regression model is sound.
Open this prompt Analysis · Advanced
Statistical Analysis Best Practices
Use this when you need practical, step-by-step guidance for designing and carrying out reliable statistical analysis in process development.
Open this prompt Learning · Advanced
Statistical Data Visualization Recommendations
Use this when you need to select appropriate statistical visualization techniques and tools for presenting process data.
Open this prompt Analysis · Intermediate
Statistical Hypothesis Testing Guidance
Use this when you need help selecting the right statistical test for your data and understanding how to conduct and interpret it correctly.
Open this prompt Analysis · Intermediate
Statistical Process Control Implementation
Use this when you need to implement SPC to monitor and control process variability in manufacturing or similar settings.
Open this prompt Planning · Intermediate
Statistical Quality Control Strategy Recommendation
Use this when you need to apply SQC methods to historical production data to improve product quality in process development.
Open this prompt Analysis · Advanced
Statistical Software Recommendations
Use this when you need recommendations for statistical software tools suitable for analyzing process development data, considering skill level and features.
Open this prompt Decisions · Beginner
Time Series Analysis and Forecasting
Use this when you have historical time-stamped data and need to analyze trends, detect seasonality, and generate forecasts for future values.
Open this prompt Analysis · Intermediate