Everything to know about Hypothesis testing

Francisca — Wed, 16 Oct 2019 12:50:41 +0000

Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data.

Hypothesis testing is about testing to see whether the stated hypothesis is acceptable or not. During this hypothesis testing, we will gather as much data as we can so that we can validate our hypothesis one way or another. The general idea of hypothesis testing involves:

Making an initial assumption.
Collecting evidence (data).
Based on the available evidence (data), deciding whether to reject or not reject the initial assumption.

There are four steps to conducting a proper hypothesis test:

Step One: Formulate the Hypothesis

The first step is that of writing the hypothesis. You actually have two hypotheses to write. One is called the null hypothesis. Think of this as the hypothesis that states how you would expect things to work without any external factors to change it. The other hypothesis is called the alternative hypothesis. This is the hypothesis that shows a change from the null hypothesis that is caused by something. For Example:

In hypothesis testing, we just test to see if our data fits our alternative hypothesis or if it fits the null hypothesis. We don't worry about what is causing our data to shift from the null hypothesis if it does. Keep in mind, when writing your null hypothesis and alternative hypothesis, they must be written in such a way so that if the null hypothesis is false, then the alternative hypothesis is true and vice versa.

Step Two: t-statistics

The test statistic is a single measure that captures the statistical nature of the relationship between observations you are dealing with. The test statistic depends fundamentally on the number of observations that are being evaluated. It differs from situation to situation.

You construct a test statistics, and you compare it to a critical value(or values) to determine whether the null hypothesis should be rejected. The specific test statistics and critical value(s) depend on which population parameter is being tested, the size of the sample being used, and other factors.

Step Three: Cut-off Value for t-statistics

Determine the critical value by finding the value of the known distribution of the test statistic such that the probability of making a Type I error - which is denoted α (Greek letter "alpha") and is called the "significance level of the test" - is small (typically 0.01, 0.05, or 0.10). Formular for t-cutoff;

t-cutoff = +/- |T.INV(α/2, n-1)

Step Four: Check If t-statistics falls in the rejection region

Compare the test statistic to the critical value. If the test statistic is more extreme in the direction of the alternative than the critical value, reject the null hypothesis in favor of the alternative hypothesis. If the test statistic is less extreme than the critical value, do not reject the null hypothesis.

Errors in Hypothesis Testing

We make our decision based on evidence not on 100% guaranteed proof. Again:

If we reject the null hypothesis, we do not prove that the alternative hypothesis is true.
If we do not reject the null hypothesis, we do not prove that the null hypothesis is true.

We merely state that there is enough evidence to behave one way or the other. This is always true in statistics! Because of this, whatever the decision, there is always a chance that we made an error.

Note that, in statistics, we call the two types of errors by two different names - one is called a "Type I error," and the other is called a "Type II error." Here are the formal definitions of the two types of errors:

Type I Error
The null hypothesis is rejected when it is true.
Type II Error
The null hypothesis is not rejected when it is false.

There is always a chance of making one of these errors.

Summary

Key terms and concepts:

Null hypothesis: Null hypothesis is a statistical hypothesis that assumes that the observation is due to a chance factor. The null hypothesis is denoted by; H0: μ1 = μ2, which shows that there is no difference between the two population means.
Alternative hypothesis: Contrary to the null hypothesis, the alternative hypothesis shows that observations are the result of a real effect.
Level of significance: Refers to the degree of significance in which we accept or reject the null-hypothesis. 100% accuracy is not possible for accepting or rejecting a hypothesis, so we, therefore, select a level of significance that is usually 5%.
Type I error: When we reject the null hypothesis, although that hypothesis was true. Type I error is denoted by alpha. In hypothesis testing, the normal curve that shows the critical region is called the alpha region.
Type II errors: When we accept the null hypothesis but it is false. Type II errors are denoted by beta. In Hypothesis testing, the normal curve that shows the acceptance region is called the beta region.
One-tailed test: When the given statistical hypothesis is one value like H0: μ1 = μ2, it is called the one-tailed test.
Two-tailed test: When the given statistics hypothesis assumes a less than or greater than value, it is called the two-tailed test.

Thanks for reading!

The World Of Product Managers(PM)

Francisca — Fri, 21 Jun 2019 21:01:28 +0000

Product Managers basically manage a product through its lifetime.PM's do different things depending on the type of company but I'm going to talk about PM's in a tech-related company.

So, basically I was in a tech company recently and I heard the word Product Managers and I was like..... what's that. I reached out to one of the PM's to ask what it was all about and what they actually do especially in a tech-related company.

A PM in a tech-related company works like this: For example, company A approaches with a banking app idea. Product Managers look into the requirement and flow they come up with users stories, do more research on banking apps and look at best ideas, they analyze the user stories and ideas from other sources, then join these ideas together and create a full feature list.
Next, are the app flow and design process, architecture diagram and sketches(wireframes). At this point a proposal is set and sent to company A, pitch the idea and get feedback on their requirement.
After a successful delivery and company A is okay with the requirement, PM's develop mock-ups and prototype then straight to the coding phase this where the developers/programmers come into place. Product Managers still write tickets(issues), plan spirits, remove blockers for the developers and also decide which features are built first.

Product Managers are in various capacities like :

Managing all stakeholders of the product(client, developer, QA, sales team and finance, etc)
Detailed requirement gathering and business analysis
Ideas generation and problem-solving
Removing blockers for stakeholders involved
Planning and deciding the life cycle of the product
Testing the product( if there is no software Quality Assurance)

And a lot of other silent activities. Product Managers are more than this but this majorly the core things of what's done

In tech, PM's are of various types:

Design-Oriented Product Managers
Data-Oriented Product Managers
Developer-Oriented Product Managers

The day-to-day work of a PM is time-consuming between brainstorming new features, doing customer interviews, working with various internal/external stakeholders trying to create a go-to-market plan.

If you find that the features your team is building aren't successful for some reasons you should probably hire a dedicated PM.
A good PM decides what a company needs to build next and also creates a desired outcome and more company's product closer to its vision