Hypothesis Testing Analysis Dashboard

Hypothesis Testing Analysis

Applying Statistical Methods to Organisational Scenarios for Data-Driven Decision Making

Comprehensive Visual Guide

Problem Statement

Define the business question

→

Hypotheses

Set null & alternative hypotheses

→

Statistical Test

Select appropriate test method

→

Analysis

Execute test & interpret results

→

Business Impact

Provide actionable insights

Ex
Loan Affordability Assessment
One-tailed t-test

Objective: Test if cosmetics salesperson's commission meets loan requirements (£501 average)

H₀: Mean commission ≤ £500/month
Hₐ: Mean commission > £500/month
Result: Reject H₀ (p = 1.36e-05)
Commission sufficient for loan approval
1
Product Price Comparison
Independent two-sample t-test

Objective: Compare average product prices between Store A and competitor Store B

H₀: No significant price difference
Hₐ: Significant price difference exists
Result: Fail to reject H₀ (p = 0.151)
No significant price difference found
2
Employee Productivity Analysis
One-way ANOVA

Objective: Compare productivity levels across Sales, Marketing, and Finance departments

H₀: No productivity difference between departments
Hₐ: Significant productivity differences exist
Result: Reject H₀ (p = 7.45e-18)
Significant productivity differences identified
3
Market Research Analysis
Chi-square test for independence

Objective: Test relationship between age groups and social media platform preferences

H₀: Age and platform preference are independent
Hₐ: Age and platform preference are related
Result: Fail to reject H₀ (p = 0.441)
No relationship found between variables
4
Product Quality Control
Chi-square test for proportions

Objective: Compare defective product proportions across three production lines

H₀: No quality difference between production lines
Hₐ: Significant quality differences exist
Result: Reject H₀ (p = 0.004)
Quality varies significantly across lines
5
Product Line Strategy
One-way ANOVA

Objective: Compare revenue potential between fiction and non-fiction books

H₀: No revenue difference between genres
Hₐ: Significant revenue differences exist
Result: Reject H₀ (p = 8.96e-13)
Non-fiction generates higher revenue
Key Findings & Conclusions
6
Total Scenarios Analysed
5
Statistical Tests Applied
4
Significant Results Found
67%
Success Rate

Loan Assessment Success

Commission data supports loan approval with statistical significance (p < 0.001), providing confidence for lending decision.

Competitive Pricing Parity

No significant price differences found between stores, suggesting effective competitive positioning in the market.

Departmental Productivity Gaps

Highly significant differences in productivity across departments (p < 0.001) highlight need for targeted interventions.

Universal Social Media Appeal

Social media preferences are independent of age groups, supporting broad-based marketing strategies.

Production Line Quality Issues

Significant quality variations identified (p < 0.01), requiring immediate quality control improvements.

Genre Revenue Potential

Non-fiction books demonstrate significantly higher revenue potential (p < 0.001), guiding inventory investment.

Business Implications & Recommendations
💰

Financial Services: Loan Decision Framework

Implement statistical validation for loan applications using commission data analysis. This evidence-based approach reduces risk whilst supporting qualified applicants, improving both approval rates and loan performance.

🛍️

Retail Strategy: Competitive Intelligence

Current pricing strategy maintains competitive parity. Focus resources on value proposition differentiation rather than price wars, ensuring sustainable profit margins whilst remaining competitive.

👥

Human Resources: Performance Optimisation

Significant productivity gaps require immediate attention. Implement department-specific improvement programmes, resource reallocation, and best practice sharing to optimise overall organisational efficiency.

📱

Marketing Strategy: Cross-Demographic Approach

Platform preferences transcend age boundaries, supporting unified marketing campaigns. Allocate budgets based on platform engagement metrics rather than demographic assumptions for maximum ROI.

🏭

Manufacturing: Quality Management System

Implement immediate quality control measures for underperforming production lines. Focus on Line B improvement whilst studying Line C's success factors for organisation-wide implementation.

📚

Retail Investment: Genre Portfolio Strategy

Prioritise non-fiction inventory investment based on superior revenue performance. Maintain balanced portfolio whilst weighting budget allocation towards higher-performing categories for maximum profitability.

Analytical Process Reflection

This comprehensive analysis demonstrates the power of statistical hypothesis testing in organisational decision-making. By systematically applying appropriate statistical methods—from t-tests to ANOVA and chi-square analyses—we transformed business questions into actionable insights. The process involved careful hypothesis formulation, rigorous test selection based on data characteristics, and clear interpretation of results within business contexts. Each scenario required critical thinking to match statistical methods with organisational needs, ensuring recommendations were both statistically sound and commercially viable. This evidence-based approach enables organisations to make confident decisions backed by robust analytical foundations.