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Pearson correlation - calculate required sample size with G*Power

// Pearson correlation - calculate required sample size with G*Power // The pearson correlation tests two variables for a linear relationship. In the run-up to an empirical study or data collection for the pearson correlation, the necessary sample size must be determined, for example using G*Power. The minimum sample size depends on one- or two-sided testing, the assumed effect (aka. correlation coefficient), the alpha level, the statistical power and the the null hypothesis correlation (usually assumed to be 0). At the end, I show examples of different minumum sample size for the pearson correlation with different characteristics of the input parameters mentioned using G*Power Download link: ============ https://www.psychologie.hhu.de/arbeit... Introduction to G*Power: ==================== 🎥    • G*Power: A (short) Beginner's Guide   ⏰ Timestamps: ============== 0:00 Introduction 0:10 Selecting the pearson correlation 0:17 Select type of power analysis 0:25 Input parameter I: One-sided or two-sided 0:39 Input parameter II: Assumed correlation coefficient 1:01 Input parameter III: Alpha error probability 1:17 Input parameter IV: power (1-beta error) 1:45 Input parameter V: Null hypothesis correlation 1:53 Calculation and overview If you have any questions or suggestions regarding pearson correlation - calculate required sample size with G*Power, please use the comment function. Thumbs up or down to decide if you found the video helpful. #useR #statorials Support channel? 🙌🏼 =================== Paypal donation: https://www.paypal.com/paypalme/Bjoer... Amazon affiliate link: https://amzn.to/49BqP5H