Visualization

HypoTestX provides several helpers for plotting test results and distributions. All visualization functions require matplotlib (and some support the optional plotly backend):

pip install matplotlib
# or, for matplotlib + plotly together:
pip install hypotestx[visualization]

---

result.plot()

Every HypoResult has a .plot() method that auto-selects the best chart type for the test:

import hypotestx as hx

result = hx.ttest_2samp(group1, group2)
fig = result.plot()           # auto
fig = result.plot(kind="bar")          # grouped bar with CI
fig = result.plot(kind="box")          # box plot
fig = result.plot(kind="p_value")      # p-value on null distribution
fig.savefig("result.png")

kind	Description
"auto"	Best chart type for the test (default)
"bar"	Mean ± 95 % CI bar chart for two-group tests
"box"	Box plots of group distributions
"p_value"	Test statistic highlighted on the null distribution

---

plot_result()

Standalone function — equivalent to result.plot():

from hypotestx.explore.visualize import plot_result
# or via public API (if exposed in __init__.py)
import hypotestx as hx

fig = hx.plot_result(result, kind="auto")
fig.show()

---

plot_distributions()

Plot the raw distributions of one or more groups:

import hypotestx as hx

fig = hx.plot_distributions(
    [group1, group2],
    labels=["Control", "Treatment"],
    kind="box",            # "box" (default) | "bar" | "violin"
    title="Group Comparison",
)
fig.show()

Parameter	Type	Default	Description
groups	list[list[float]]	—	Data for each group
labels	list[str] | None	None	Group labels
kind	str	"box"	Chart type: "box", "bar", "violin"
title	str | None	None	Plot title

---

plot_p_value()

Visualise a p-value on the null distribution for a given test statistic:

import hypotestx as hx

fig = hx.plot_p_value(
    p_value=0.023,
    alpha=0.05,
    test_statistic=2.41,
    alternative="two-sided",
)
fig.show()

The rejection region is highlighted in red; the critical value lines and the test statistic are annotated.

---

generate_report()

Generate a formatted report — HTML, PDF, or plain text:

import hypotestx as hx

result = hx.ttest_2samp(group1, group2)

# Return as string
html = hx.generate_report(result, fmt="html")
text = hx.generate_report(result, fmt="text")

# Write to file
hx.generate_report(result, path="report.html", fmt="html")
hx.generate_report(result, path="report.pdf",  fmt="pdf")   # requires weasyprint
hx.generate_report(result, path="report.txt",  fmt="text")

fmt	Description	Extra requirement
"html"	HTML with embedded chart (if matplotlib is installed)	None
"pdf"	PDF via WeasyPrint	pip install weasyprint
"text"	Plain text summary	None

---

Reporting module helpers

For lower-level control:

from hypotestx.reporting.generator import export_html, export_pdf, export_csv

# Single result to HTML file
export_html(result, path="report.html")

# PDF (requires weasyprint)
export_pdf(result, path="report.pdf")

# Multiple results to CSV
export_csv([result1, result2, result3], path="results.csv")

---

Notes

• All plot_* functions return a matplotlib.figure.Figure object. Call .show() to display interactively or .savefig(path) to write to disk.

• If matplotlib is not installed, these functions raise an ImportError with a helpful install message.

• plot_effect_size(), plot_assumptions(), and generate_apa_report() from older documentation are not yet implemented. Use result.plot(), plot_distributions(), and apa_report() respectively.