Decoding the P-Value: Your Key to Understanding Model Fit

We always hear a lot about p-value <0.5 when we build an Excel model or SPSS model. The multiple questions that run in the minds of students revolve around which factor to consider first before concluding if a model can be considered impactful. The statistical significance of a model is measured through multiple elements or factors like p-value, R square, adjusted R square, VIF, and ANOVA.

The first and foremost place to look at is the p-value. The base rule is to look for a p-value less than 0.05. But what does this mean? It means that there is a 5% chance that the results from the data set in consideration is random. 95% of the time the result is statistically significant. A p-value does not explicitly measure the magnitude of the hypothesis, but it denotes the level of confidence in the result’s consistency across random data samples.

Why 0.05? It is a valid question in the minds of statisticians or researchers. The convention of p =0.05 dates back to the work of statistician Ronald Fisher. Since then, the rule of 0.05 has been widely used. While 0.05 is used as a rule, it must be noted that the size of the dataset, the context of the problem also matters. If the data is extremely huge, a p-value less than 0.05 may not exactly qualify as rejecting the null hypothesis.

The null hypothesis is that the independent variable or the factors do not impact the dependent variable. If the p-value is less than 0.05, then the null hypothesis could be rejected. To elaborate the null hypothesis denotes that there is no effect, difference in proportions or impact due to independent variables.

The below statement is an example of null hypothesis for marketing analytics.

In terms of marketing analytics, the hypothesis could be constructed as follows:

APPLICATION OF HYPOTHESIS TESTING IN MARKETING ANALYTICS

ASSUMPTION: A Marketing analyst suspects that the conversion rate increases when a new website design is implemented. The change on the website may contain better navigation or use of SEO best practices like keywords or meta descriptions.

UPDATION OF WEBSITE DESIGN

APPLICATION OF HYPOTHESIS TESTING IN FINANCIAL ANALYTICS

ASSUMPTION: A financial analyst suspects that the stock price listed on the NYSE (New York Stock Exchange) reacts significantly to the quarterly earnings announcements of the corresponding company.

STOCK FLUCTUATION DUE TO QUARTERLY ANNOUNCEMENTS

This may be due to new information available during earning announcements by the company. A merger or sudden hike in profit or acquisition or a product release may be a few of the valid factors that cause an impact on the stock price.

APPLICATION OF P VALUE IN PRODUCT ANALYTICS

ASSUMPTION: A product analyst believes that  the introduction of  a new feature like  AI powered assistant increases user productivity.The product analyst could measure if the introduction of AI-powered assistants impacts the number of tasks completed by the users within the platform.

INTRODUCTION OF AI ASSISTANT IN TEAMS/ZOOM

This may be due to the AI assistant’s capacity to automatically find and fix a meeting time that could work for all participants, suggest priority tasks based on communication patterns, and suggest content for email communication based on historical interaction.