Aprenda Visualizing Experiment Results | Product Experimentation and Hypothesis Testing

Clear communication is essential when sharing experiment results with stakeholders, and visualizing your findings is one of the most effective ways to achieve this. For product teams, well-crafted charts and graphs make it easier to interpret experiment outcomes, spot trends, and quickly understand whether a variant outperformed the control group. When you transform raw data into visuals, you help everyone—from engineers to executives—grasp the impact of your product decisions at a glance.


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import matplotlib.pyplot as plt

# Example conversion rates for control and variant groups
groups = ["Control", "Variant"]
conversion_rates = [0.12, 0.16]

plt.bar(groups, conversion_rates, color=["skyblue", "lightgreen"])
plt.ylabel("Conversion Rate")
plt.title("A/B Test Conversion Rates")
plt.ylim(0, 0.2)
plt.show()

Choosing the right chart for your experiment data is crucial. For A/B test results, bar charts are often the best choice because they clearly compare metrics like conversion rates between groups. Line charts might be used if you want to show changes over time, while box plots can help convey the spread or variability in your data. Always match your chart type to the story you want to tell—simple, direct visuals make it easier for stakeholders to draw accurate conclusions.


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import matplotlib.pyplot as plt

groups = ["Control", "Variant"]
conversion_rates = [0.12, 0.16]

plt.bar(groups, conversion_rates, color=["skyblue", "lightgreen"])
plt.ylabel("Conversion Rate")
plt.title("A/B Test Conversion Rates")
plt.ylim(0, 0.2)

# Add annotation to highlight the difference
plt.text(
    1, 0.16, "↑ +0.04", 
    ha="center", va="bottom", fontsize=12, color="darkgreen", weight="bold"
)
plt.show()

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 6

Pergunte à IA

Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo

Suggested prompts:

Can you explain how to interpret the results shown in the bar chart?

What other types of charts could I use for different experiment data?

How can I add more groups or variants to the chart?

Deslize para mostrar o menu


              1234567891011
            
import matplotlib.pyplot as plt

# Example conversion rates for control and variant groups
groups = ["Control", "Variant"]
conversion_rates = [0.12, 0.16]

plt.bar(groups, conversion_rates, color=["skyblue", "lightgreen"])
plt.ylabel("Conversion Rate")
plt.title("A/B Test Conversion Rates")
plt.ylim(0, 0.2)
plt.show()


              12345678910111213141516
            
import matplotlib.pyplot as plt

groups = ["Control", "Variant"]
conversion_rates = [0.12, 0.16]

plt.bar(groups, conversion_rates, color=["skyblue", "lightgreen"])
plt.ylabel("Conversion Rate")
plt.title("A/B Test Conversion Rates")
plt.ylim(0, 0.2)

# Add annotation to highlight the difference
plt.text(
    1, 0.16, "↑ +0.04", 
    ha="center", va="bottom", fontsize=12, color="darkgreen", weight="bold"
)
plt.show()

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 6

Visualizing Experiment Results

1. What type of chart best shows A/B test results?

2. How can annotations help in experiment result presentations?

3. Which matplotlib function is used to add text to a chart?

Visualizing Experiment Results

1. What type of chart best shows A/B test results?

2. How can annotations help in experiment result presentations?

3. Which matplotlib function is used to add text to a chart?