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The popular word in the info subject that folks usually skip
Even though finding out Data Science, I am positive you will encounter many unfamiliar conditions. Most of the statistical term that exists in the book or on the net courses would mention the word with little appropriate rationalization.
A person of the terms I frequently hear without elaborate definition is Parametric and Non-Parametric. This term was used in quite a few Information Science learning modules, but most of the time just brushed away. Additionally, statistics generally have the similar terms that refer to diverse issues.
I want to reveal Parametric and Non-Parametric conditions in this post, specially in info science and studies. Let us get into it.
When you research the Parametric Figures on the Net, the to start with point revealed is the Parametric Statistic Approach. So, what is the parametric system?
We can explain the parametric approach as a statistical system class that utilizes particular likelihood distribution with set parameters. It signifies the process relies on the distribution assumptions built of the information.
For case in point, a t-take a look at is a test to assess if the indicates concerning the group is considerable or not. As the take a look at would review both equally the team indicates, the exam would think that the information follows a ordinary distribution. Deviation from this assumption could undermine the take a look at end result.
Let’s use a sample dataset to use the t-check. I would use the titanic dataset from the Seaborn package deal for this sample.
#Import necessary packages
import pandas as pd
import seaborn as sns
from scipy.stats import ttest_ind, shapirotitanic = sns.load_dataset('titanic')
#We would divide the team into two according to the survival
#and we want to review the fare
not_survived_fare = titanic[titanic['survived'] == ]['fare']
survived_fare = titanic[titanic['survived'] == 1 ]['fare']
Prior to we test to do the t-check, we want to see if the data comply with the normal distribution. There are a several approaches to evaluate it, but we can use the Shapiro-Wilk exam as one particular of the tactics. Let’s see if the not surviving fare follows the usual distribution.
# Test the normality of the not survived fare facts
stat, p = stats.shapiro(not_survived_fare)
print('Statistics=%.3f, p=%.3f' % (stat, p))
alpha = .05
if p < alpha:
print('Fail to Reject H0 (Data distributed Normal)')
else:
print('Reject H0 (Data not distributed Normal)')
The data follows the normal distribution how about the other dataset group?
stat, p = stats.shapiro(survived_fare)
print('Statistics=%.3f, p=%.3f' % (stat, p))
alpha = 0.05
if p < alpha:
print('Fail to Reject H0 (Data distributed Normal)')
else:
print('Reject H0 (Data not distributed Normal)')
It seems the data also follow the normal distribution. Let’s do a t-test to see if there is a significant difference between both group’s means.
t_stat, p = ttest_ind(survived_fare, not_survived_fare)print('Statistics=%.3f, p=%.3f' % (t_stat, p))
alpha = 0.05
if p < alpha:
print('Fail to Reject H0 (Significant)')
else:
print('Reject H0 (Not-Significant)')
There are significant differences between the fare, as the t-test shows the evidence.
There are various examples of Parametric Statistic, including:
- ANOVA (Analysis of Variance)
- Linear regression
- Logistic regression
- Pearson correlation
Every single one of them relies on specific data distribution. That is why they are considered Parametric.
In contrast with Parametric statistics, the non-parametric statistic does not make assumptions regarding the underlying data distribution. It does not estimate population parameters and uses ranking or other non-parametric measures. It doesn’t matter if the data follow a normal distribution or not the reliability is still the same.
Non-parametric is more robust to outliers because the technique makes no assumption.
But, parametric tests were more powerful because the data followed the assumed distribution, and to achieve the same power level as a parametric test, a non-parametric might require a larger sample size. Additionally, the non-parametric was not as effective as the parametric test in detecting slight differences between the groups.
Nevertheless, the usefulness of the non-parametric test is still great when the data distribution violates the parametric test assumption.
Let’s take an example of a non-parametric test Mann-Whitney U. It’s the non-parametric test equivalent of a t-test. Mann-Whitney U test is based on the data ranking rather than the data parameter itself.
Let’s use the Python example to understand better.
from scipy.stats import mannwhitneyu# perform Mann-Whitney U test
u_stat, p = mannwhitneyu(not_survived_fare, survived_fare)
print('Statistics=%.3f, p=%.3f' % (u_stat, p))
alpha = 0.05
if p < alpha:
print('Fail to Reject H0 (Significant)')
else:
print('Reject H0 (Not-Significant)')
There are still significant differences between the two groups, as shown in the result above.
Non-parametric test techniques include the following but are not limited to:
- Wilcoxon signed-rank test
- Kruskal-Wallis test
- Friedman test
- Spearman’s rank correlation coefficient
- Kendall’s tau correlation coefficient
In summary, parametric statistics assume a specific distribution and estimate the population parameters. In contrast, non-parametric statistics have no assumption to the data distribution and use non-parametric measurement (such as ranking).
Parametric statistics could be more powerful if we met the assumptions, but violations were easier to disturb the assumption. On the other hand, non-parametric statistics are more robust to violations but less powerful when the parametric assumptions are met.
I hope it helps!
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