When Pressed against time, 3 quickies to choose the right Statistical Test: 1. Check your Research Question. 2. Study the Research Design again (Conclusive or Experimental) 3. Evaluate the Data Characteristic (Continuous or Categorical

Key Steps 1. Identify data type • Categorical or continuous • Normal or non-normal distribution 2. Define research objective • Comparing groups • Testing relationships • Measuring differences

When time’s ticking and your data is staring at you like an impatient toddler, the key is to channel your inner statistical ninja: first, ask yourself, “What’s the goal?”—comparing groups? Relationships? Predicting the future? (No pressure.) If you’re comparing means, t-tests or ANOVA might be your jam; for relationships, correlation or regression is your BFF. Categorical data? Chi-square to the rescue! And if your data looks like it partied too hard and isn’t normally distributed, non-parametric tests are your chill, go-with-the-flow friends. Remember, the right test is like the right coffee order—efficient, effective, and saves you from a meltdown. Now go forth and p-value like a pro!

To choose the right statistical test efficiently when pressed for time, follow these steps systematically: Selecting the Right Statistical Test – A Flowchart Approach: Step 1 - Identify your goal: Comparing groups? (Go to Step 2), Finding associations? (Use Pearson/Spearman), or Predicting outcomes? (Use Linear/Logistic/Mixed-effects regression). Step 2 - Is the outcome Continuous? (Go to Step 3) or Categorical? (Go to Step 4). Step 3 - Use t-test/ANOVA (independent) or paired t-test/Repeated Measures ANOVA (repeated). Step 4 - Use Chi-square/Fisher’s (independent) or McNemar’s/Cochran’s Q (repeated). Step 5 - If correlated data, use LMM/GLMM/GEE. 🚀 #Statistics #DataScience #Biostatistics

Tight deadlines, complex data, and the pressure to deliver meaningful insights make choosing the right statistical test overwhelming. Here’s a quick framework I rely on: 1) Know Your Data: Pinpoint whether the data is numerical, categorical, or ordinal to quickly narrow down test options. 2) Clarify Your Goal: Are you comparing groups, exploring relationships, or making predictions? Your objective decides the test. 3) Check Assumptions: If your data doesn’t meet parametric requirements like normality, opt for non-parametric tests. 4) Use a Cheat Sheet: A handy reference chart for common tests can save you hours. 5) Tap into Tools: Tools like Python or Excel often suggest suitable tests—don’t hesitate to use them! #DataAnalysis