Python is a versatile programming language that offers various ways to solve problems. In this article, we will explore different approaches to solve the problem of calculating and applying probability in Python 3.6.

## Approach 1: Using the random module

```
import random
def calculate_probability():
probability = random.random()
return probability
result = calculate_probability()
print("The calculated probability is:", result)
```

In this approach, we utilize the random module in Python to generate a random number between 0 and 1. This random number represents the probability. The `random()`

function returns a float value between 0 and 1, inclusive of 0 but exclusive of 1. We then return this probability and print it as the output.

## Approach 2: Using the statistics module

```
import statistics
def calculate_probability():
data = [0, 1]
probability = statistics.mean(data)
return probability
result = calculate_probability()
print("The calculated probability is:", result)
```

In this approach, we utilize the statistics module in Python to calculate the probability. We create a list of data containing the possible outcomes (0 and 1 in this case). The `mean()`

function from the statistics module calculates the average of the data, which represents the probability. We then return this probability and print it as the output.

## Approach 3: Using the numpy module

```
import numpy as np
def calculate_probability():
probability = np.random.rand()
return probability
result = calculate_probability()
print("The calculated probability is:", result)
```

In this approach, we utilize the numpy module in Python to generate a random number between 0 and 1. The `rand()`

function from the numpy module returns a random float value between 0 and 1. We then return this probability and print it as the output.

After exploring these three approaches, it can be concluded that the best option depends on the specific requirements of the problem. If you need a simple random number between 0 and 1, the first approach using the random module is sufficient. However, if you require more advanced statistical calculations or need to work with arrays, the second or third approach using the statistics or numpy module respectively would be more suitable.