Crunch Numbers from the Terminal: A Quick Guide to Stat CLI

Most of us default to Excel or R when running stats. But what if you could fire up a t-test or ANOVA right from your terminal, without booting heavy software? That’s exactly what stat-cli does: a lightweight command-line tool for quick statistical analysis.

Before we dive in, let’s clear up the basics: there are three common types of t-tests you’ll see in practice.

Test Type	When to Use	Example
One-sample t-test	Compare the mean of a single sample against a known value.	Do students’ average test scores differ from 75?
Independent t-test	Compare means between two independent groups.	Do Control vs Treatment groups differ in scores?
Paired t-test	Compare means of the same group at two different times (repeated measure).	Did students improve from pre-test to post-test?

👉 In this post, we’ll focus on the independent t-test (two groups) and then move on to ANOVA (three or more groups).

📖 What is ANOVA?

ANOVA stands for Analysis of Variance. While a t-test only compares two groups, ANOVA lets you test whether the means of three or more groups are significantly different.

• Null hypothesis (H₀): all group means are equal.
• Alternative hypothesis (H₁): at least one group mean differs.

It works by comparing the variance between groups (how far the group means are from the overall mean) to the variance within groups (how spread out values are inside each group). The ratio of these is the F-statistic. A large F with a small p-value means the group differences are unlikely due to chance.

👉 If ANOVA shows significance, you usually follow up with post-hoc tests (like Tukey’s HSD) to see which specific groups differ.

⬇️ Download

Download Stat CLI here

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Example 1 — Independent Samples t-test

Step 1 — Prepare Your Data

Save your data in a spreadsheet (.xlsx). Here’s a toy dataset of test scores from two groups:

Student	Group	Score
A	Control	72
B	Control	65
C	Control	70
D	Control	68
E	Control	74
F	Treatment	80
G	Treatment	85
H	Treatment	82
I	Treatment	78
J	Treatment	90

Save this as scores.xlsx.

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Step 2 — Run the CLI

stat-cli

You’ll be prompted to pick:

1. Dependent variable → choose Score
2. Grouping variable → choose Group
3. Statistical test → choose t-test (two groups)

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Step 3 — Get Results

Running t-test on Score grouped by Group...

Control (n=5): mean = 69.8, std = 3.4
Treatment (n=5): mean = 83.0, std = 4.6

t = -5.84, df = 8, p = 0.0004
Result: Significant difference between groups (p < 0.05).

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Example 2 — One-way ANOVA

Step 1 — Prepare Data with Three Groups

Student	Group	Score
A	Group1	55
B	Group1	60
C	Group1	58
D	Group1	62
E	Group2	70
F	Group2	68
G	Group2	72
H	Group2	66
I	Group3	80
J	Group3	85
K	Group3	78
L	Group3	82

Save this as anova_scores.xlsx.

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Step 2 — Run the CLI

stat-cli

This time, choose:

1. Dependent variable → Score
2. Grouping variable → Group
3. Statistical test → One-way ANOVA

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Step 3 — Get Results

Running One-way ANOVA on Score grouped by Group...

Group1 (n=4): mean = 58.8, std = 2.6
Group2 (n=4): mean = 69.0, std = 2.6
Group3 (n=4): mean = 81.3, std = 3.1

F(2, 9) = 65.4, p < 0.0001
Result: Significant difference between groups (p < 0.05).

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Why This Matters

• ⚡ Fast — no GUI, no imports, just raw stats in seconds.
• 📦 Portable — runs anywhere you can install Python.
• 🧠 Focused — does exactly what you need without bloat.

If you’re tired of bloated spreadsheets but don’t want the overhead of full R/Python scripts, stat-cli is your middle ground.

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👉 In the next post, we’ll cover Chi-square tests with categorical data. Stay tuned.