Service Description: Advanced Statistical Analysis (SPSS & Python) Transform raw biological data into statistically significant insights. In the Life Sciences, the integrity of your conclusion depends entirely on the rigor of your analysis. Our consultancy provides expert statistical support tailored for biological, clinical, and environmental research. We bridge the gap between complex experimental design and publication-quality results using industry-standard tools. Our Technical Approach SPSS for Clinical & Social Sciences Ideal for clinical trials, surveys, and experimental biology where structured data requires validated statistical tests. Descriptive & Inferential Statistics: T-tests, ANOVA/MANOVA, and Chi-square tests. Regression Modeling: Linear, logistic, and non-linear regression to identify predictors. Non-Parametric Testing: For datasets that do not follow a normal distribution. Python for High-Throughput & Big Data For bioinformatics, proteomics, or ecological modeling where datasets are too large or complex for traditional software. Custom Scripting: Automated data cleaning and "wrangling" using Pandas and NumPy. Data Visualization: Publication-ready plots (Heatmaps, Volcanos, PCA) using Matplotlib and Seaborn. Machine Learning: Implementing predictive models or clustering algorithms via Scikit-learn. Key Service Features Experimental Design Consultation: We help you determine the correct sample size (Power Analysis) before you even start your experiments. Hypothesis Testing: Rigorous validation of your research questions to ensure $p < 0.05$ (or your field’s specific threshold) is handled with integrity. Results Interpretation: We don’t just give you the numbers; we provide a written summary explaining what the statistics mean for your biological findings. Assumption Checking: Ensuring your data meets the requirements for normality.