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Hypothesis Testing is one of the most straightforward approaches for the Scrum Master or Product Owner to integrate data analytics into their decision-making processes. It is to disprove any preconceived idea by determining how accurate the decision can be. It offers a more straightforward way to verify if the outcome will be valid or not. Most CSPO professionals use Hypothesis Testing tools for their data mining endeavors. In this article, we will explore everything related to Hypothesis Testing.

The Definition Of Hypothesis Testing:

Hypothesis Testing is a useful statistical tool for an Agile Leader to identify if the outcome of an endeavor will be as expected or not. The Agilist tests an idea/assumption surrounding a population data parameter and conducts the analysis. It involves performing a Null Hypothesis Process and an alternative hypothesis using sample data. 

The Formula of Hypothesis Testing:

Depending upon the data availability and population size, the Agile Data Analyst uses different Hypothesis Testing techniques and determines the null hypothesis outcome. Here is the hypothesis testing formula:

z = (¯x−μ)/(σ/√n) [x= sample mean, μ= population mean, σ= population standard deviation, n= size of the sample]

t = (¯x−μ)/(s/√n) [s= sample standard deviation]

χ^2=∑(Oi−Ei)^2/Ei [Oi= observed value, Ei= expected value]

Types of Hypothesis Testing:

Hypothesis Testing has different types to suit other requirements. Here are the most used Hypothesis Testing types for you!

1. Z-Test: Z-test statistical method is used toA to test if the means of different population samples are the same or different when the variance value is known. This type of Hypothesis Testing is done on normally distributed data. It can also handle more significant than 30 sample sizes while following the central limit theorem.
2. Normality Testing: This testing is used for a population sample’s normal distribution. If the resulting data points stay around the mean value, the probability of them being equally likely should be above or below the mean value. You can resemble its shape like a bell curve, equally distributed on both sides of the mean.
3. T-Test: T-Type of Hypothesis Testing is used for small sample sizes with a normally distributed population. Alongside Sprint Planning, the can use the standard deviation technique to conduct a T-Test. It is very effective to find the population curve’s confidence intervals. 

Hypothesis Testing Examples For You:

1. Does adding a new product feature increase customer engagement?

Hypothesis: Adding a new feature to our product will increase customer engagement.

Riskiest assumption: Customers will value and use the new feature.

Test: Conduct a randomized controlled trial (RCT) where a subset of users are given access to the new feature while the control group does not have access. Measure customer engagement metrics for both groups, such as time spent using the product or the number of actions taken.

Success criteria: Users with access to the new feature show a statistically significant increase in engagement compared to the control group.

Data needed: User engagement data from the RCT.

Outcome: If the hypothesis is true, the new feature should be rolled out to all users. If it is false, the feature should be re-evaluated or removed.

2. Will reducing the price of a product lead to an increase in sales?

Hypothesis: Reducing the price of our product will lead to an increase in sales.

Riskiest assumption: Price is the main barrier to purchase for potential customers.

Test: Conduct a price sensitivity analysis by testing different price points for the product and measuring the resulting sales volume.

Success criteria: A statistically significant increase in sales at the lower price point.

Data needed: Sales data for different price points.

Outcome: If the hypothesis is true, then the price should be lowered. If it is false, the price point should be re-evaluated or other factors affecting sales should be considered.

3. Does changing the packaging of a product increase brand appeal?

Hypothesis: Changing the packaging of our product will increase brand appeal.

Riskiest assumption: Customers find the current packaging unappealing or unattractive.

Test: Conduct a survey with a sample of customers, asking them to rate the appeal of the current packaging and a proposed new packaging design.

Success criteria: A statistically significant increase in the rating of the proposed new packaging design compared to the current packaging.

Data needed: Survey data on customer perception of packaging design.

Outcome: If the hypothesis is true, then the packaging should be changed. If it is false, the packaging design should be re-evaluated, or other factors affecting brand appeal should be considered.

How to design a Hypothesis Test Card with Strategyzer

A hypothesis test card is used in business strategy development to validate assumptions about a specific market or customer segment. The card is designed to guide the process of testing a hypothesis and determining whether it is true or false. The following are some steps you can follow to design a hypothesis test card:

• Define the hypothesis: The first step is to define the view you want to test. An idea is a statement explaining a particular phenomenon or behavior you want to try.
• Identify the riskiest assumption: The next step is to identify the most challenging assumption underpinning your hypothesis. The most dangerous assumption is the one that has the most significant potential to invalidate your hypothesis.
• Design the test: The third step is to design a test to validate your hypothesis. The test should be designed to minimize the risk of bias and ensure reliable results.
• Set success criteria: The fourth step is to set success criteria. You will use this metric to determine whether your hypothesis is true or false. The success criteria should be specific, measurable, and time-bound.
• Identify the data needed: The fifth step is identifying the necessary data to test your hypothesis. This could include data from customer interviews, surveys, or other sources.
• Plan the test: The sixth step is to plan the trial. This involves identifying the target audience, designing the test questions, and determining how the data will be collected and analyzed.
• Execute the test: The seventh step is to execute the test. This involves conducting interviews, surveys, or other data collection methods to gather the necessary data.
• Analyze the results: The eighth step is to analyze the results. This involves reviewing the data to determine whether your hypothesis is true or false.
• Update the hypothesis: The ninth and final step is to update the theory based on the test results. If the idea is true, you can move forward with your strategy. You may need to revise your plan or return to the drawing board if it is false.

Summing Up:

To bring Agile Transformation using Scrum, the Scrum Master, Product Owner, and Developers must follow the combining practices of Hypothesis Testing and Continuous Delivery Cycle. Here are the key takeaways:

• Hypothesis Testing is for Data Analysts, Scrum Master, and Product Owner.
• It helps professionals assess an assumption’s plausibility by using sample data sets.
• It provides insightful evidence related to the hypothesis plausibility of the given data.
• It involves measuring, analyzing, and examining the “population” data.

It will help them accelerate their experimentation while amplifying the validated Learning. This also accelerates innovative decision-making and tests whether the problem is solvable.

Reference

1. Hypothesis Testing - Definition, Examples, Formula, Types (cuemath.com)