A/B Testing and Hypothesis Testing: LinkedIn Premium Subscription and Job Search Success Rate

This repository contains an experimental study designed to analyze the impact of LinkedIn Premium subscription on job search success rate through A/B testing and hypothesis testing. Additionally, the study examines the correlation between networking activity on LinkedIn and job success rate.

Project Overview

The study aimed to answer the following questions:

1. Is there a significant association between having a LinkedIn Premium subscription increase a user's job search success rate through LinkedIn?
2. Is there a significant association between networking activity on LinkedIn and job success rate?

Key Features:

• Independent Samples T-Test Analysis: Conducted to compare job success rates between LinkedIn Premium users and non-Premium users.
• Correlation Analysis: Evaluated the relationship between networking activity (e.g., connections, messages) and job search success.
• Tools Used: IBM SPSS for statistical tests, power analysis and results interpretation.

Methods and Techniques

1. Experimental Design:

   • Designed a t-test to compare job success rates between two groups: LinkedIn Premium users and non-Premium users.
   • Defined Job Success as a continuos variable on a Likert Scale varing between No response, connection, r

2. Statistical Tests:

   • T-Test: Performed to compare means of job success rates between LinkedIn Premium users and non-users.
   • Correlation Analysis: Used to assess the relationship between networking activity on LinkedIn and job success rates.
   • Power Analysis: Ensured the sample size was adequate to detect a statistically significant effect if one exists.

3. Data Analysis Tools:

   • IBM SPSS: Used for data manipulation, A/B testing, and hypothesis testing.

Results

• T-Test Results: Found a significant difference in job success rates between LinkedIn Premium users and non-users (p-value < 0.05).
• Correlation Analysis: A positive correlation was observed between networking activity and job success rate, indicating that higher networking activity may be associated with greater job success.
• Power Analysis: Verified that the sample size was adequate to detect an effect with high confidence.