Python vs G*Power: Sample size calculation for Pearson correlation

Here I compare the power analysis for sample size calculation for Pearson correlation.

The codes can be seen as follows:
import pingouin as pg

# Parameters
r = 0.3 # expected correlation coefficient
alpha = 0.05 # significance level
power = 0.80 # desired power

# Calculate sample size

print(f"Required sample size: {sample_size:.2f}")

# for one-sided test
# Define the parameters
r = 0.3 # expected correlation coefficient
alpha = 0.05 # significance level
power = 0.80 # desired power
alternative = 'greater' # alternative hypothesis for one-sided test

# Calculate the required sample size

print(f"Required sample size: {sample_size:.2f}")

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