import scipy.stats as stats # Given values margin_of_error = 0.025 confidence_level = 0.95 likely_range = (0.2, 0.3) # Calculate the midpoint of the likely range for the observed proportion p = (likely_range[0] + likely_range[1]) / 2 # Calculate the Z-score corresponding to the confidence level z = stats.norm.ppf((1 + confidence_level) / 2) # Calculate the required sample size n = ((z**2) * p * (1 - p)) / (margin_of_error**2) n = round(n) # Round to the nearest integer print("Required sample size:", n)