csv_inspection

A local file has been selected for analysis:
"/Users/babaji/Downloads/data.csv"

Load and inspect this file now.

I'll load the selected file and show a quick preview.

</>Load data and inspect

import pandas as pd

df = pd.read_csv("/Users/babaji/Downloads/data.csv")

print(f"Shape: {df.shape}")
columns = df.columns.tolist()
print(f"Columns ({len(columns)}):", columns if len(columns) <= 100 else columns[:100] + ["..."])

df.head()

Shape: (52, 2)
Columns (2): ['Gender', 'Verbal_reasoning_score']

	Gender	Verbal_reasoning_score
0	Female	23
1	Female	24
2	Female	25
3	Female	25
4	Female	27

The file contains 52 records with two fields: Gender and Verbal_reasoning_score. The preview shows female observations with verbal-reasoning scores recorded as integers, beginning in the low-to-mid 20s. The dataset is structured for comparing score distributions or summary statistics across gender groups.

What is the best test for comaring the males and females for verbal_reasonong_score given the data

Thinking

Assessing statistical tests

I think we need to assess normality and variance first. The best option for comparing two independent groups with continuous scores is likely the independent samples t-test (Welch). However, if the data isn't normal or has outliers, we might consider the Mann Whitney test. I'll need to write code to inspect the group sample size summaries and distributions. The Welch t-test is typically the best choice, so I want to verify that before moving forward.

For two independent groups (male vs. female) and a numeric verbal-reasoning score, the primary test is usually a Welch independent-samples t-test. It compares mean scores without assuming the two groups have equal variances.

First, check group sizes, descriptive statistics, and distributional shape; this determines whether a nonparametric alternative such as Mann–Whitney U is more appropriate.

</>Summarize scores by gender

import seaborn as sns
import matplotlib.pyplot as plt

display(
    df.groupby("Gender")["Verbal_reasoning_score"]
      .agg(n="count", mean="mean", std="std", median="median", min="min", max="max")
      .round(2)
)

plt.figure(figsize=(7, 4))
sns.boxplot(data=df, x="Gender", y="Verbal_reasoning_score")
sns.stripplot(
    data=df, x="Gender", y="Verbal_reasoning_score",
    color="black", alpha=0.65, jitter=0.12
)
plt.title("Verbal Reasoning Scores by Gender")
plt.xlabel("Gender")
plt.ylabel("Verbal reasoning score")
plt.show()

Use an independent-samples comparison, because the female and male scores come from separate participants. A Welch two-sample t-test is the best default: it tests whether the group means differ and does not require equal variances.

The observed difference is very large: females average 26.46 versus 11.85 for males, a difference of 14.62 points. The group standard deviations differ somewhat (3.87 vs. 5.05), supporting Welch’s version rather than the equal-variance Student t-test. With 26 observations in each group, the t-test is generally robust to modest non-normality.

If the score distribution is strongly non-normal or there are influential outliers, use the Mann–Whitney U test as a nonparametric sensitivity check. It tests whether scores tend to be higher in one group, rather than specifically comparing means.

So which test should I use

Use a Welch independent-samples t-test.

It compares the mean verbal-reasoning scores for two independent groups and is preferred over the standard pooled t-test because the group standard deviations differ (Female: 3.87; Male: 5.05).

</>Compare verbal scores by gender

from scipy.stats import ttest_ind

female_scores = df.loc[df["Gender"] == "Female", "Verbal_reasoning_score"]
male_scores = df.loc[df["Gender"] == "Male", "Verbal_reasoning_score"]

result = ttest_ind(female_scores, male_scores, equal_var=False)
result

TtestResult(statistic=np.float64(11.714120423775809), pvalue=np.float64(1.6065744761161111e-15), df=np.float64(46.837557205754045))

Use the Welch independent-samples t-test.

It is appropriate because verbal-reasoning score is numeric and the Female and Male observations are independent groups. Welch’s version is preferable because the groups have different standard deviations.

The test result, $t(46.84)=11.71$, $p \approx 1.61 \times 10^{-15}$, provides extremely strong evidence that mean verbal-reasoning scores differ by gender in this sample. Female scores are higher on average (26.46) than male scores (11.85).