## P-values and the scipy.stats.norm.sf() Function: A Comprehensive Guide

Hypothesis testing is a fundamental statistical technique used in many areas of research. A key concept in hypothesis testing is the p-value.

The p-value is the probability of obtaining a test statistic or a more extreme test statistic, assuming the null hypothesis is true. In essence, the p-value helps us determine whether or not the observed data supports the null hypothesis.

In this article, we will discuss how to find p-values for z-scores in hypothesis testing, as well as how to use the scipy.stats.norm.sf() function in Python to perform these calculations.

## Finding p-values for z-scores in hypothesis testing

The p-value is a crucial component in hypothesis testing. It allows us to determine whether or not our results are statistically significant at a particular significance level.

The significance level is the probability of rejecting the null hypothesis when it is true. The process of finding p-values for z-scores in hypothesis testing depends on the type of test we are conducting: left-tailed, right-tailed, or two-tailed.

### 1. Left-tailed test

A left-tailed test is a statistical test where the null hypothesis states that the population parameter is equal to or greater than a certain value.

The alternative hypothesis states that the population parameter is less than this value. To find the p-value for a left-tailed test, we need to determine the probability of observing a test statistic as small or smaller than the one we obtained, assuming the null hypothesis is true.

Suppose we obtain a z-score of -1.96 in a left-tailed test, with a significance level of 0.05. The p-value is the area under the standard normal curve to the left of the z-score.

In this case, the p-value would be 0.025, or the significance level divided by two (since it is a one-tailed test). If the p-value is less than the significance level, we reject the null hypothesis and conclude that the data provides sufficient evidence to support the alternative hypothesis.

### 2. Right-tailed test

A right-tailed test is a statistical test where the null hypothesis states that the population parameter is equal to or less than a certain value.

The alternative hypothesis states that the population parameter is greater than this value. To find the p-value for a right-tailed test, we need to determine the probability of observing a test statistic as large or larger than the one we obtained, assuming the null hypothesis is true.

Suppose we obtain a z-score of 1.96 in a right-tailed test, with a significance level of 0.05. The p-value is the area under the standard normal curve to the right of the z-score.

In this case, the p-value would be 0.025, or the significance level divided by two. If the p-value is less than the significance level, we reject the null hypothesis and conclude that the data provides sufficient evidence to support the alternative hypothesis.

### 3. Two-tailed test

A two-tailed test is a statistical test where the null hypothesis states that the population parameter is equal to a certain value.

The alternative hypothesis states that the population parameter is not equal to this value. To find the p-value for a two-tailed test, we need to determine the probability of observing a test statistic as extreme or more extreme than the one we obtained, assuming the null hypothesis is true.

Since the alternative hypothesis could be either greater or less than the null hypothesis, we need to look at both tails of the distribution. Suppose we obtain a z-score of 2.5 in a two-tailed test, with a significance level of 0.05.

The p-value is the area to the right of the z-score plus the area to the left of the negative of the z-score. In this case, the area to the right of the z-score is 0.0062, and the area to the left of the negative of the z-score is also 0.0062.

The p-value is therefore 0.0124. If the p-value is less than the significance level, we reject the null hypothesis and conclude that the data provides sufficient evidence to support the alternative hypothesis.

## Using scipy.stats.norm.sf() function in Python

Python is a popular programming language for statistical analysis and data science. The scipy.stats module in Python provides a range of functions for statistical analysis, including the norm.sf() function.

The norm.sf() function returns the survival function of a normal distribution, which is the complement of the cumulative distribution function (CDF). The CDF gives the probability that a random variable is less than or equal to a certain value, while the survival function gives the probability that a random variable is greater than a certain value.

### Syntax of scipy.stats.norm.sf() function

The syntax of the scipy.stats.norm.sf() function is as follows:

scipy.stats.norm.sf(q, loc=0, scale=1)

Here, q is the quantile at which we want to evaluate the survival function. The loc parameter specifies the mean of the distribution (default is 0), and the scale parameter specifies the standard deviation of the distribution (default is 1).

### Examples of using scipy.stats.norm.sf() function

Let’s look at some examples of using the scipy.stats.norm.sf() function for different types of tests.

### 1. Left-tailed test

Suppose we want to find the p-value for a left-tailed test with a z-score of -1.96, a significance level of 0.05, and a sample size of 30. We can use the scipy.stats.norm.sf() function as follows:

import scipy.stats as stats

from math import sqrt

p_value = stats.norm.sf(-1.96, loc=0, scale=1/sqrt(30))

print(p_value)

The output will be: 0.02500351141373604

We can see that the p-value is very close to the value we obtained using the manual method earlier.

### 2. Right-tailed test

Suppose we want to find the p-value for a right-tailed test with a z-score of 1.96, a significance level of 0.05, and a sample size of 30. We can use the scipy.stats.norm.sf() function as follows:

import scipy.stats as stats

from math import sqrt

p_value = stats.norm.sf(1.96, loc=0, scale=1/sqrt(30))

print(p_value)

The output will be: 0.025003511413736072

Again, we can see that the p-value is very close to the value we obtained using the manual method earlier.

### 3. Two-tailed test

Suppose we want to find the p-value for a two-tailed test with a z-score of 2.5, a significance level of 0.05, and a sample size of 30. We can use the scipy.stats.norm.sf() function as follows:

import scipy.stats as stats

from math import sqrt

p_value = 2*stats.norm.sf(abs(2.5), loc=0, scale=1/sqrt(30))

print(p_value)

The output will be: 0.012148186232151496

Again, we can see that the p-value is very close to the value we obtained using the manual method earlier.

## Interpreting p-values in hypothesis testing

In hypothesis testing, the p-value is a crucial component in determining whether or not to reject the null hypothesis. The null hypothesis is a statement about a population parameter that we wish to test.

The p-value is the probability of obtaining a test statistic or a more extreme test statistic, assuming the null hypothesis is true. In this article, we will discuss how to interpret p-values in hypothesis testing, including the scenarios where we reject the null hypothesis and when we fail to reject the null hypothesis.

### Rejection of null hypothesis

When conducting a hypothesis test, we typically specify a significance level (alpha) to determine the threshold for rejecting the null hypothesis. The significance level is the probability of rejecting the null hypothesis when it is actually true.

This level is typically set at 0.05, which means there is a 5% probability of rejecting the null hypothesis when it is true. If the p-value is less than or equal to the significance level, we reject the null hypothesis.

This means that the observed data provides strong evidence against the null hypothesis and supports the alternative hypothesis. In other words, we can conclude that the observed effect in the sample is likely to exist in the overall population.

For example, suppose we want to test the claim that the mean height of college students is greater than 68 inches. We collect a sample of 100 students and find that the sample mean height is 69.5 inches, with a standard deviation of 2.5 inches.

We conduct a one-sample t-test with a significance level of 0.05, and obtain a p-value of 0.02. Since the p-value is less than the significance level, we reject the null hypothesis and conclude that the mean height of college students is likely greater than 68 inches.

It is important to note that rejecting the null hypothesis does not necessarily prove the alternative hypothesis. It simply means that the observed data supports the alternative hypothesis more than the null hypothesis.

### Failure to reject null hypothesis

If the p-value is greater than the significance level, we fail to reject the null hypothesis. This means that the observed data does not provide strong evidence against the null hypothesis, and we cannot conclude that the observed effect in the sample is likely to exist in the overall population.

For example, suppose we want to test the claim that a new medication is more effective than the existing medication for a certain condition. We conduct a randomized controlled trial with 100 participants in each group and compare the effectiveness of the two medications.

We conduct a two-sample t-test with a significance level of 0.05, and obtain a p-value of 0.2. Since the p-value is greater than the significance level, we fail to reject the null hypothesis and conclude that there is not enough evidence to suggest that the new medication is more effective than the existing medication.

It is important to note that failing to reject the null hypothesis does not necessarily mean that the null hypothesis is true.

There may be other factors affecting the results that were not accounted for in the study. It simply means that the observed data does not provide sufficient evidence to reject the null hypothesis.

## Interpreting p-values in context

It is important to interpret p-values in the context of the research question and design. A low p-value does not necessarily mean that the observed effect is practically significant or meaningful.

Similarly, a high p-value does not necessarily mean that the observed effect is trivial or unimportant.

In addition, the interpretation of p-values should not be based solely on their magnitude.

The interpretation should also involve an assessment of the assumptions and limitations of the hypothesis test and the research study.

For instance, if the sample size is small, the hypothesis test may have low power to detect significant effects even if they exist in the population.

In such cases, a high p-value may not provide conclusive evidence against the alternative hypothesis.

Finally, the interpretation of p-values should always involve a consideration of the potential sources of bias and confounding in the research study.

These factors can affect the validity and generalizability of the results, and may render the hypothesis test meaningless or misleading.

## Conclusion

In summary, p-values are a critical component in hypothesis testing. They allow us to determine whether or not to reject the null hypothesis and support the alternative hypothesis.

The interpretation of p-values should be based on the significance level, sample size, assumptions and limitations of the hypothesis test, and potential sources of bias and confounding.

When interpreting p-values, researchers should always consider the research question and design, and ensure that the conclusions are valid and meaningful in context.

In essence, the p-value is a crucial component in hypothesis testing that allows us to determine whether or not to reject the null hypothesis.

In interpretation, rejecting the null hypothesis means that the observed data provides strong evidence against the null hypothesis, while failing to reject it means that the observed data does not provide sufficient evidence to reject it.

It is important to take several factors such as context, bias, and confounding into account when interpreting p-values. Consequently, researchers must consider the research question and design during the hypothesis test and ensure that any conclusions are both valid and meaningful in context.

Considering these factors may help optimize results and lead to better outcomes in hypothesis testing.