# Exploring Gender Differences in Multitasking: A Conceptual Analysis

Published: 22 May 2024| Version 1 | DOI: 10.17632/t2zyv8vmd4.1
Contributor:
Sunil Maria Benedict

## Steps to reproduce

import numpy as np import matplotlib.pyplot as plt from scipy.stats import ttest_ind # Define aspects of multitasking aspects = ['Task Switching Speed', 'Attention Allocation', 'Task Completion Time'] # Simulated data for women and men women_data = { 'Task Switching Speed': np.random.normal(0.8, 0.1, 50), 'Attention Allocation': np.random.normal(0.8, 0.1, 50), 'Task Completion Time': np.random.normal(10, 2, 50) } men_data = { 'Task Switching Speed': np.random.normal(0.6, 0.1, 50), 'Attention Allocation': np.random.normal(0.6, 0.1, 50), 'Task Completion Time': np.random.normal(12, 2, 50) } # Compute summary statistics for each aspect summary_statistics = {} p_values = {} for aspect in aspects: women_mean = np.mean(women_data[aspect]) men_mean = np.mean(men_data[aspect]) women_median = np.median(women_data[aspect]) men_median = np.median(men_data[aspect]) women_std = np.std(women_data[aspect]) men_std = np.std(men_data[aspect]) summary_statistics[aspect] = { 'Women Mean': women_mean, 'Men Mean': men_mean, 'Women Median': women_median, 'Men Median': men_median, 'Women Standard Deviation': women_std, 'Men Standard Deviation': men_std } # Perform hypothesis testing (t-test) t_stat, p_value = ttest_ind(women_data[aspect], men_data[aspect]) p_values[aspect] = p_value # Print summary statistics and hypothesis testing results for aspect, stats in summary_statistics.items(): print(f"Aspect: {aspect}") print(f"Women Mean: {stats['Women Mean']}, Men Mean: {stats['Men Mean']}") print(f"Women Median: {stats['Women Median']}, Men Median: {stats['Men Median']}") print(f"Women Standard Deviation: {stats['Women Standard Deviation']}, Men Standard Deviation: {stats['Men Standard Deviation']}") print(f"p-value: {p_values[aspect]}") print("") # Generate histograms for each aspect for aspect in aspects: plt.figure(figsize=(8, 6)) plt.hist(women_data[aspect], bins=20, alpha=0.5, color='red', label='Women') plt.hist(men_data[aspect], bins=20, alpha=0.5, color='blue', label='Men') plt.title(f'{aspect} Comparison between Women and Men') plt.xlabel('Performance') plt.ylabel('Frequency') plt.legend() plt.grid(True) plt.show()

Independent

## Categories

Psychology, Gender Studies, Women's Studies, Career Development of Women