Related Jobs

1. Sampling Distributions

🔹 2. Type I and Type II Errors

🧑‍💼 Job Roles

• Data Analyst
  
  

• Statistician
  
  

• Quality Analyst
  
  

• Research Analyst
  
  

📌 How used at work

• Estimating population parameters from samples
  
  

• Controlling false alarms (Type I error) and missed detections (Type II error)
  
  

• Decision-making under uncertainty
  
  

🔹 3. Large Sample Tests (Z-tests)

🔹 Test of Significance for:

• Single proportion
  
  

• Difference of proportions
  
  

• Single mean
  
  

• Difference of means
  
  

🧑‍💼 Job Roles

• Business Analyst
  
  

• Market Research Analyst
  
  

• Operations Analyst
  
  

• Policy Analyst
  
  

📌 How used at work

• Comparing customer conversion rates
  
  

• Testing average sales, salaries, or production output
  
  

• Comparing performance of two processes or strategies
  
  

These topics are core tools in inferential statistics and are used in many data‑driven careers, especially where decisions depend on samples, uncertainty, and hypothesis testing.​

Key job roles

• Data Analyst / Business Analyst
  Uses sampling distributions, z‑tests, t‑tests, and chi‑square tests to compare means, proportions, and categorical variables for business decisions in domains like marketing, operations, and HR.​

• Data Scientist / Machine Learning Engineer
  Applies hypothesis tests (t, F, chi‑square) to validate features, compare models, check assumptions, and design A/B experiments before and after deploying models.​

• Statistician / Applied Statistician
  Designs experiments and surveys, chooses suitable large‑sample or small‑sample tests, and interprets Type I/II errors in research, government projects, and consulting.​

• Biostatistician / Clinical Trial Analyst
  Uses tests for means, proportions, and chi‑square tests of independence/goodness of fit to evaluate treatment effects, side‑effects, and risk factors in medicine and public health.