# Decoding Spearman Rank Correlation: Interpreting the Results

## Definition of correlation and correlation coefficient

Correlation is a statistical technique that measures the strength and direction of a linear relationship between two variables. The most commonly used correlation measure is the Pearson correlation coefficient.

It is a measure of the degree to which two variables are linearly related to each other, ranging from -1 to +1. A correlation coefficient of +1 means that the two variables have a perfect positive relationship, i.e., when one variable increases, the other variable also increases.

In contrast, a correlation coefficient of -1 indicates a perfect negative relationship, i.e., when one variable increases, the other variable decreases. The Pearson correlation coefficient of 0 implies that there is no linear relationship between the two variables.

## Spearman Rank Correlation

Spearman Rank Correlation is a non-parametric measure of correlation, which means it does not assume any underlying distribution of data. It is used when the data is in the form of ranked variables rather than continuous variables.

In other words, when we want to determine if there is a relationship between the two variables, but we do not have a clear idea of what their values might be, Spearman Rank Correlation is the way to go.

Imagine that we have two sets of data, math exam scores, and science exam scores, for a group of students.

We rank both sets of exam scores from highest to lowest and assign the rank to each score. Then, we can calculate the Spearman Rank Correlation coefficient to determine whether there is a relationship between the two sets of data.

## Creating a pandas DataFrame

Now that we have a better understanding of Spearman Rank Correlation, let’s see how it can be calculated in Python. First, we need to create a pandas DataFrame that contains our data.

### We can use the following code to create a DataFrame containing math and science exam scores for ten students:

``````import pandas as pd
data = {'math_scores': [90, 85, 72, 95, 80, 86, 78, 92, 88, 75],
'science_scores': [88, 80, 70, 94, 85, 79, 81, 90, 89, 76]}
df = pd.DataFrame(data)

print(df)``````

### Output:

``````   math_scores  science_scores
0           90              88
1           85              80
2           72              70
3           95              94
4           80              85
5           86              79
6           78              81
7           92              90
8           88              89
9           75              76``````

## Using the spearmanr() function

We can now use the spearmanr() function provided by the scipy.stats module to calculate the Spearman Rank Correlation coefficient. This function returns two values, the correlation coefficient, and the corresponding p-value.

### Let’s calculate the Spearman Rank Correlation coefficient between math and science exam scores:

``````from scipy.stats import spearmanr
spearman_corr, p_value = spearmanr(df['math_scores'], df['science_scores'])
print('Spearman Rank Correlation Coefficient:', spearman_corr)
print('p-value:', p_value)``````

### Output:

``````Spearman Rank Correlation Coefficient: 0.4117647058823529
p-value: 0.24320498178983267``````

We can see that the Spearman Rank Correlation coefficient is 0.41, suggesting that there is a weak positive correlation between math and science exam scores. Moreover, the p-value is 0.243, indicating that there is no significant correlation between the two variables.

## Conclusion:

In conclusion, correlation is a crucial statistical measure used to describe the relationship between two variables. Spearman Rank Correlation is a non-parametric measure of correlation that can be used when the data is in the form of ranked variables.

Python provides a convenient way to calculate both Pearson correlation coefficient and Spearman Rank Correlation coefficient. As always, it is essential to understand the strengths and limitations of the method chosen.

With this knowledge and tools, we can make more informed decisions and improve our analysis processes.

## Interpreting the correlation coefficient

The Spearman Rank Correlation coefficient ranges from -1 to +1, just like the Pearson correlation coefficient. The interpretation of the coefficient, however, differs from that of the Pearson correlation coefficient as it deals with ranked variables.

If the Spearman Rank Correlation coefficient is -1, it indicates a perfect negative correlation between the two variables. This means that when one variable ranks high, the other variable ranks low and vice versa.

If the Spearman Rank Correlation coefficient is +1, it signifies a perfect positive correlation between the two variables. This means that when one variable ranks high, the other variable ranks high as well and vice versa.

If the Spearman Rank Correlation coefficient is 0, it suggests that there is no correlation between the two variables, i.e., when one variable ranks high, there is no pattern in how the other variable ranks. If the Spearman Rank Correlation coefficient is positive, it indicates a positive correlation between the two variables.

This means that when one variable ranks high, the other variable tends to rank high as well. The closer the coefficient is to +1, the stronger the positive correlation.

If the Spearman Rank Correlation coefficient is negative, it indicates a negative correlation between the two variables. This means that when one variable ranks high, the other variable tends to rank low and vice versa.

The closer the coefficient is to -1, the stronger the negative correlation.

## Significance of the correlation

The p-value associated with the Spearman Rank Correlation coefficient provides information about the significance of the correlation. The p-value represents the probability that the correlation is due to chance.

A p-value less than 0.05 is commonly used to determine whether a correlation is statistically significant or not. It means that there is a less than 5 percent chance that the correlation is due to chance.

If the p-value is less than 0.05, we can reject the null hypothesis that there is no correlation between the two variables.

On the other hand, a p-value greater than 0.05 indicates that the correlation is not statistically significant, and we fail to reject the null hypothesis that there is no correlation between the two variables.

It does not necessarily mean that there is no connection between the two variables; it merely means that we cannot consider the correlation statistically significant. In such situations, it may be worth exploring other variables that could have a significant impact on the relationship between the two variables.

It’s worth noting that a small p-value does not necessarily mean that the correlation is significant. If the sample size is small, it can lead to a low p-value by chance, even if the correlation exists only in the sample.

Therefore, it is essential to consider the sample size when interpreting the p-value. The smaller the sample size, the greater the uncertainty in the correlation estimate, and the higher the possibility of chance correlations.

## Conclusion:

Interpreting the results of correlation analysis is critical to draw meaningful conclusions and avoid misinterpretations. We have explored the interpretation of the Spearman Rank Correlation coefficient, which can range from -1 to +1, depending on the strength and direction of the relationship between the two variables.

We also discussed the significance of the correlation, determined by the p-value. Keep in mind that the correlation coefficient and p-value provide an indication of the relationship between the variables, but they do not prove a cause-and-effect relationship.

They only indicate the presence of a relationship that may require further investigation.

Understanding correlation results can help in making informed decisions and drawing meaningful conclusions from the data. Always keep in mind that the correlation coefficient and p-value indicate a relationship, but they do not prove a cause-and-effect relationship.

Therefore, it’s essential to explore the possible underlying factors further for better analysis and decision-making.