1598769840

# The Relationship between Significance, Power, Sample Size

Congratulations, your experiment has yielded significant results! You can be sure (well, 95% sure) that the independent variable influenced your dependent variable. I guess all you have left to do is write up your discussion and submit your results to a scholarly journal. Right…………?

Obtaining significant results is a tremendous accomplishment in itself self but it does not tell the entire story behind your results. I want to take this time and discuss statistical significance, sample size, statistical power, and effect size, all of which have an enormous impact on how we interpret our results.

## Significance (p = 0.05)

First and foremost, let’s discuss statistical significance as it forms the cornerstone of inferential statistics. We’ll discuss significance in the context of true experiments as it is the most relevant and easily understood. A true experiment is used to test a specific hypothesis(s) we have regarding the causal relationship between one or many variables. Specifically, we hypothesize that one or more variables (ie. independent variables) produce a change in another variable (ie. dependent variable). The change is our inferred causality. If you would like to learn more about the various research design types visit my article (LINK).

For example, we want to test a hypothesis that an authoritative teaching style will produce higher test scores in students. In order to accurately test this hypothesis, we randomly select 2 groups of students that get randomly placed into one of two classrooms. One classroom is taught by an authoritarian teacher and one taught by an authoritative teacher. Throughout the semester, we collect all the test scores among all the classrooms. At the end of the year, we average all the scores to produce a grand average for each classroom. Let’s assume the average test score for the authoritarian classroom was 80%, and the authoritative classroom was 88%. It would seem your hypothesis was correct, the students taught by the authoritative teacher scored on average 8% higher on their tests compared to the students taught by the authoritarian teacher. However, what if we ran this experiment 100 times, each time with different groups of students do you think we would obtain similar results? What is the likelihood that this effect of teaching style on student test scores occurred by chance or another latent (ie. unmeasured) variable? Last but not least, is 8% considered “high enough” to be that different from 80%?

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1598769840

## The Relationship between Significance, Power, Sample Size

Congratulations, your experiment has yielded significant results! You can be sure (well, 95% sure) that the independent variable influenced your dependent variable. I guess all you have left to do is write up your discussion and submit your results to a scholarly journal. Right…………?

Obtaining significant results is a tremendous accomplishment in itself self but it does not tell the entire story behind your results. I want to take this time and discuss statistical significance, sample size, statistical power, and effect size, all of which have an enormous impact on how we interpret our results.

## Significance (p = 0.05)

First and foremost, let’s discuss statistical significance as it forms the cornerstone of inferential statistics. We’ll discuss significance in the context of true experiments as it is the most relevant and easily understood. A true experiment is used to test a specific hypothesis(s) we have regarding the causal relationship between one or many variables. Specifically, we hypothesize that one or more variables (ie. independent variables) produce a change in another variable (ie. dependent variable). The change is our inferred causality. If you would like to learn more about the various research design types visit my article (LINK).

For example, we want to test a hypothesis that an authoritative teaching style will produce higher test scores in students. In order to accurately test this hypothesis, we randomly select 2 groups of students that get randomly placed into one of two classrooms. One classroom is taught by an authoritarian teacher and one taught by an authoritative teacher. Throughout the semester, we collect all the test scores among all the classrooms. At the end of the year, we average all the scores to produce a grand average for each classroom. Let’s assume the average test score for the authoritarian classroom was 80%, and the authoritative classroom was 88%. It would seem your hypothesis was correct, the students taught by the authoritative teacher scored on average 8% higher on their tests compared to the students taught by the authoritarian teacher. However, what if we ran this experiment 100 times, each time with different groups of students do you think we would obtain similar results? What is the likelihood that this effect of teaching style on student test scores occurred by chance or another latent (ie. unmeasured) variable? Last but not least, is 8% considered “high enough” to be that different from 80%?

#type-i-error #power-analysis #statistical-significance #sample-size #effect-size #data analysis

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## Comparing Power BI with other tools

the Business Intelligence (BI) world has been moving towards self-service BI. As expected, several vendors created tools empowering regular users to gain insights from their data. Among the many, there is Power BI. Nowadays, users want to understand the differences between Product X and Power BI.

One of the most common questions in conferences and user group sessions is likely, “can you provide a comparison between this product and Power BI?”.

The answer is almost always, “No, I cannot compare them, because they are too different”. First, one needs to understand the deep difference between Power BI and most other reporting tools on the market. Only later does a comparison make any sense. As a matter of fact, I think Power BI can be compared to only a few products on the market today. I would like to add my point of view to the discussion.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Indeed, Power BI is a tremendously powerful data modeling tool that happens to come with a pretty face; most other products are beautifully crafted reporting tools with a pretty face. The only thing they have in common is the pretty face. If you stop at what they have in common, you are only comparing a small fraction of the whole product, and that would be unfair.

To go further, a deeper understanding of basic BI concepts is needed.

Beware: this article is biased. I love Power BI and I make my living out of it. Nevertheless, I am a BI professional; I started working with Business Intelligence many years ago and I have gathered experience that I can share. I will try to be as fair as I can in this post, as my goal is not to provide a comparison with any tool. The goal of this post is to help you understand what you really need to evaluate when making (or reading) any comparison between different BI products.

At the top level, any Business Intelligence solution is composed of three layers:

Raw data: these are the data sources that one wants to analyze. Raw data comes as is.
Semantic model: this is where data is re-arranged to optimize it for analysis. Here you also define the calculations required by the reports.
Reports: these are the nice dashboards you can build with the tool.

Power BI manages all three layers: you start from raw data, you can build a semantic layer, and finally you prepare reports. Most other reporting tools are focused on the last layer and are limited in the previous two. In other words, they are missing the capability to build a real semantic layer. It is important to clarify what a semantic layer is, to understand what you would miss by choosing a different product.

In the old ages of BI, there was a clear separation between users and developers. A BI developer would build a project to help users extract insights from their data, and build reports. Users did not need to understand tables, relationships, or calculations. The developer oversaw shaping the tables, providing predefined calculations and giving sensible names to entities. Leveraging the semantic model, users did not have to know DAX, MDX or SQL.

A semantic model lets users interact with business entities like customers, sales, and products. Users would place those entities in reports made with Excel or with other reporting tools. Regular users were happy with just Excel and a Pivot Table. More advanced users wanted more powerful tools, and this led to the creation of several reporting tools with their ad-hoc programming language to create more advanced formulas. Regardless, the important thing is that no matter how powerful those tools are, they were still reporting tools based on the existence of a previously crafted semantic model. No semantic model, no reporting.

Picture this: a BI tool lets a developer build a semantic model. A reporting tool lets a user build a report on top of an existing semantic model. You need both to create a BI solution.Learn more from Power BI online course

Unfortunately, building a BI project takes time. Users were hungry for reports. This led to the start of the Self-Service BI era. Self-service BI is the idea of users building reports themselves, to reduce development time and to build a democratic knowledge about data. Sounds cool and terrifying at the same time.

Anyway, this is where we are today. Obviously, driven by the market several vendors started to build self-service BI tools. A few new products appeared on the market. Rather, existing tools evolved into new ones, targeting self-service BI. Keep in mind: any self-service BI tool requires the functionalities to build both the semantic model and the report in the same tool. Thus, depending on where you start, you have two options to have an existing product evolve into a self-service product:

If you already have a semantic model tool, you need to add reporting capabilities. You need to make it easier to use, because the target is no longer a BI professional but a regular user instead.
If you already have a reporting tool, you need to add the capability to build a semantic model because your users need to massage the data and build calculations on top of the resulting model.

In both cases, in the end you obtain a tool that mixes the capabilities to create a semantic model and to build reports. After this first step, you can add tons of different features like sharing with other users, building wizards to automatically connect to other services, improving the formula language and so on. But the core is always the same: a semantic model and a reporting tool, bound together in a nice package.

Even though we consider Power BI to be a new product, it is actually the evolution of Power Pivot and Analysis Services Tabular (semantic model), Power Query (querying tool), and Power View (the first version of the reporting tool released with Excel and SharePoint). Other vendors took similar steps, with different starting points. It is fair to say that several vendors started from a reporting tool, adding the semantic model to it.

Now, if you need to compare two BI tools, you need to compare at least these two features: the semantic model and the tools to build a report.

Say you want to compare Product X with Power BI; you show me how easy it is to build a gorgeous report on top of an SQL view, much easier and much more powerful than Power BI. Cool, but you are only comparing a fraction of both products. Reporting-wise, sure, Product X is better than Power BI. But there are other considerations: can you load multiple tables in Product X? Can you build relationships between them? Can you use a programming language to author complex calculations that involve scanning different tables? All these operations belong to the semantic model. A fair comparison needs to apply to all the features.

This is what Power BI offers you:

Power Query – a data transformation tool which is easy to use and yet incredibly powerful. It can load virtually anything and join data from different sources.
A modeling environment where you can build different kinds of relationships between tables and build powerful models. It does not hurt that it runs on top of one of the fastest databases I have ever seen.
DAX – a programming language which is not easy, but lets you author nearly any query and calculation. Yes, on this I am biased for sure!
Power BI – a reporting engine which is very good in building dashboards and reports. It can also be extended with custom visuals and third-party products.

Then, there is web-based reporting and sharing, a mobile experience, the ability to load from nearly any data source in the cloud or on premises and many other useful features. Yet, the core is composed of the four features above. If you want to compare apples to apples, you need to compare at least these four parts. Be mindful: you need all of them. A tool that requires you to build a single table because it does not let you relate two tables is nothing but a nice reporting tool. Comparing it to Power BI does not make much sense to me.

Moreover, it does not come by chance that to learn Power BI, one needs to learn new programming languages. Each feature has its own language, and this is just the right thing to have.

Finally, reporting. Reporting is only the last part, even though it is the most visible one. You might find other products are better than Power BI when it comes to reporting. This is fine, if you are aware that you are only comparing a fraction of Power BI with the whole of Product X.

I love Power BI, and I would really love to see a fair comparison between Power BI and any other product. We could learn a lot from the topic. But for it to be fair, it cannot just be based on how easy it is to build a pie chart (just kidding! You are not using a pie chart, are you?). One needs to evaluate everything both products have to offer.

To get in-depth knowledge of this technology and to develop skills to make a great career in this regard one can opt for Power BI online training Hyderabad.

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## Power BI vs Tableau

In your search for a Business Intelligence (BI) or data visualization tool, you have probably come across the two front-runners in the category: Power BI and Tableau. They are very similar products, and you have to look quite closely to figure out which product might work the best for you. I work for Encore Business Solutions; a systems partner that specializes in both Power BI and Tableau. We’ve seen more than a few scenarios in which Tableau was being used when the company really should have gone with Power BI, and vice-versa. That was part of the inspiration for this side-by-side comparison.

Unfortunately, the internet is full of auto-generated and biased pages regarding which product trumps the other. The truth is, the best product depends more on you, your organization, your budget, and your intended use case than the tools themselves. It is easy to nit-pick at features like the coding language that supports advanced analysis, or the type of maps supported — but these have a minimal impact for most businesses. I’m going to do my best to stay away from these types of comparisons.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

In writing this comparison, I did a lot of research. The result was more than just this article: I also created a tool that can generate a recommendation for you based on your response to a short questionnaire. It will generate a score for both Power BI and Tableau, plus provide a few other things to think about.

Tableau Software
Founded in 2003, Tableau has been the gold-standard in data visualization for a long time. They went public in 2013, and they still probably have the edge on functionality over Power BI, thanks to their 10-year head start. There are a few factors that will heavily tip the scales in favour of Tableau, which I’ll cover in the next few paragraphs.

Tableau: Key Strengths
Let’s make one thing clear from the start: if you want the cream of the crop, all other factors aside, Tableau is the choice for you. Their organization has been dedicated to data visualization for over a decade and the results show in several areas: particularly product usability, Tableau’s community, product support, and flexible deployment options. The range of visualizations, user interface layout, visualization sharing, and intuitive data exploration capabilities also have an edge on Power BI. Tableau offers much more flexibility when it comes to designing your dashboards. From my own experience, Tableau’s functionality from an end-user perspective is much farther ahead of Power BI than the Gartner Magic Quadrant (below) would have you believe.

Tableau built their product on the philosophy of “seeing and exploring” data. This means that Tableau is engineered to create interactive visuals. Tableau’s product capabilities have been implemented in such a way that the user should be able to ask a question of their data, and receive an answer almost immediately by manipulating the tools available to them. I have heard of cases in which Tableau actually declined to pursue the business of a customer in the scenario that the customer didn’t have the right vision for how their software would be used. If you just want something to generate reports, Tableau is overkill.

Tableau is also much more flexible in its deployment than Power BI. You can install the Tableau server in any Window box without installing the SQL server. Power BI is less flexible which I will discuss in Power BI Weaknesses.

Tableau can be purchased on a subscription license and then installed either in the cloud or an on-premise server.

Finally, Tableau is all-in on data visualization, and they have their fingers firmly on the pulse of the data visualization community’s most pressing desires. You can expect significant future improvements in terms of performance when loading large datasets, new visualization options, and added ETL functions.

Tableau Weaknesses
Unfortunately, Tableau comes at a cost. When it comes to the investment required to purchase and implement Tableau – 9 times out of 10 it will be more expensive than Power BI, by a fair margin. Often, Tableau projects are accompanied by data-warehouse-building endeavours, which compound the amount of money it takes to get going. The results from building a data warehouse and then hooking up Tableau are phenomenal, but you’ll need an implementation budget of at the very least \$50k – plus the incremental cost of Tableau licenses. Learn more from Power bi online course

Of course, a data warehouse is not a requirement. Tableau connects to more systems out-of-the-box than Power BI. However, Tableau users report connecting to fewer data sources than most other competing tools. Overall, considering the investment required to implement a data warehouse is a worthy indicator of the commitment required to get the most out of Tableau.

Power BI
Power BI is Microsoft’s data visualization option. It was debuted in 2013, and has since quickly gained ground on Tableau. When you look at Gartner’s most recent BI Magic Quadrant, you’ll notice that Microsoft is basically equal to Tableau in terms of functionality, but strongly outpaces Tableau when it comes to “completeness of vision”. Indeed, the biggest advantage of Power BI is that it is embedded within the greater Microsoft stack, which contributes to Microsoft’s strong position in the Quadrant.

Power BI: Key Strengths
Though Tableau is still regarded by many in the industry as the gold standard, Power BI is nothing to scoff at. Power BI is basically comparable to all of Tableau’s bells and whistles; unless you care deeply about the manifestation and execution of small features, you’re likely to find that Power BI is fully adequate for your BI needs.

As I mentioned, one of the biggest selling points of Power BI is that it is deeply entrenched in the Microsoft stack – and quickly becoming more integrated. It’s included in Office 365, and Microsoft really encourages the use of Power BI for visualizing data from their other cloud services. Power BI is also very capable of connecting to your external sources.

Because Power BI was originally a mostly Excel-driven product; and because the first to adopt Microsoft products are often more technical users, My personal experience is that Power BI is especially suitable for creating and displaying basic dashboards and reports. My own executive team really likes being able to access KPIs from the Office portal, without having to put much time into the report’s creation, sharing, and interactivity.

Power BI’s biggest strength; however, is its rock-bottom cost and fantastic value. For a product that is totally comparable to the category leader, it’s free (included in Office 365) for basic use and \$10/user/month for a “Pro” license. This increases adoption of the product as individuals can use Power BI risk-free. For companies that don’t have the budget for a large Business Intelligence project (including a data warehouse, dedicated analysts, and several months of implementation time), Power BI is extremely attractive. Companies that are preparing to “invest” in BI are more likely to add Tableau to their list of strongly considered options.

Power BI is available on a SaaS model and on-premise; on-premise is only supported by Power BI Premium licensing.

Microsoft is also investing heavily in Power BI, and they’re closing the small gaps in their functionality extremely fast. All of those little issues some users have with Power BI are going to disappear sooner rather than later.

Power BI Weaknesses
As I’ve mentioned, Tableau still has the slight edge on Power BI when it comes to the minutiae of product functionality; mostly due to their 10-year head start. But perhaps Power BI’s greatest weakness is its lack of deployment flexibility. For Power BI on-premise you need to install the Power BI Report Server as well as the SQL Server.

I also mentioned that Tableau works well for users with large amounts of data and for users that want on-premise systems. You should be aware that there are some new features being added to Power BI via Power BI Premium that help catch Microsoft up to Tableau in the areas of large datasets and on-premise capabilities – but Power BI Premium adds significant cost, and these features are relatively new. Tableau still reigns in these areas.

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