How to test likelihood hypothesis on dataset?

[iiiioooo]

How to test the following hypothesis? Customers with larger fares are more likely to be travailing alone than smaller ones.
Using the data below, is a common statistical test appropriate for this hypothesis?

Given were comparing different segments(high vs low) of fare, surely tests using means aren't helpful? Thus is there an alternative to statistical test package for validating this hypothesis.

Common Statistical tests
> T-test: compare two groups/categories of numeric variables with small sample size​
1. one sample t-test: test the mean of one group against a constant value​
2. two sample t-test: test the difference of means between two groups​
3. paired sample t-test: test the difference of means between two measurements of the same subject​
> Z-test: compare two groups/categories of numeric variables with large sample size​
> ANOVA test: compare the difference between two or more groups/categories of numeric variables​
> Chi-Squared test: examine the relationship between two categorical variables​
> Correlation test: examine the relationship between two numeric variable​

# package
import seaborn as sns

#load dataset
df= sns.load_dataset('titanic')
df = df[['fare','alone']]

#dataset
fare alone
0 7.2500 False
1 71.2833 False
2 7.9250 True
3 53.1000 False
4 8.0500 True