OptinMon 10 - 14 Steps

Chi-Square Test of Independence Rule of Thumb: n > 5

July 15th, 2020 by

Ever hear this rule of thumb: “The Chi-Square test is invalid if we have fewer than 5 observations in a cell”.

I frequently hear this mis-understood and incorrect “rule.”

We all want rules of thumb even though we know they can be wrong, misleading, or misinterpreted.

Rules of Thumb are like Urban Myths or like a bad game of ‘Telephone’.  The actual message gets totally distorted over time.

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Three Rules of Statistical Analysis from Your Statistics Class to Unlearn

April 28th, 2020 by

There are important ‘rules’ of statistical analysis. Like

But there are others you may have learned in statistics classes that don’t serve you or your analysis well once you’re working with real data.

When you are taking statistics classes, there is a lot going on. You’re learning concepts, vocabulary, and some really crazy notation. And probably a software package on top of that.

In other words, you’re learning a lot of hard stuff all at once

Good statistics professors and textbook authors know that learning comes in stages. Trying to teach the nuances of good applied statistical analysis to students who are struggling to understand basic concepts results in no learning at all.

And yet students need to practice what they’re learning so it sticks. So they teach you simple rules of application.  Those simple rules work just fine for students in a stats class working on sparkling clean textbook data.

But they are over-simplified for you, the data analyst, working with real, messy data. 

Here are three rules of data analysis practice that you may have learned in classes that you need to unlearn.  They are not always wrong. They simply don’t allow for the nuance involved in real statistical analysis.

The Rules of Statistical Analysis to Unlearn:

1. To check statistical assumptions, run a test. Decide whether the assumption is met by the significance of that test. 

Every statistical test and model has assumptions. They’re very important. And they’re not always easy to verify.

For many assumptions, there are tests whose sole job is to test whether the assumption of another test is being met. Examples include the Levene’s test for constant variance and Kolmogorov-Smirnov test, often used for normality. These tests are tools to help you decide if your model assumptions are being met.

But they’re not definitive.

When you’re checking assumptions, there are a lot of contextual issues you need to consider: the sample size, the robustness of the test you’re running, the consequences of not meeting assumptions, and more.

What to do instead:

Use these test results as one of many pieces of information that you’ll use together to decide whether an assumption is violated.

2. Delete outliers that are 3 or more standard deviations from the mean.

This is an egregious one. Really. It’s bad.

Yes, it makes the data look pretty. Yes, there are some situations in which it’s appropriate to delete outliers (like when you have evidence that it’s an error). And yes, outliers can wreak havoc on your parameter estimates.

But don’t make it a habit. Don’t follow a rule blindly.

Deleting outliers because they’re outliers (or using techniques like Winsorizing) is a great way to introduce bias into your results or to miss the most interesting part of your data set.

What to do instead:

When you find an outlier, investigate it. Try to figure out if it’s an error. See if you can figure out where it came from.

3. Check Normality of Dependent Variables before running a linear model

Q-Q plot and histogramIn a t-test, yes, there is an assumption that Y, the dependent variable, is normally distributed within each group. In other words, given the group as defined by X, Y follows a normal distribution.

ANOVA has a similar assumption: given the group as defined by X, Y follows a normal distribution.

In linear regression (and ANCOVA), where we have continuous variables, this same assumption holds. But it’s a little more nuanced since X is not necessarily categorical. At any specific value of X, Y has a normal distribution. (And yes, this is equivalent to saying the errors have a normal distribution).

But here’s the thing: the distribution of Y as a whole doesn’t have to be normal.

In fact, if X has a big effect, the distribution of Y, across all values of X, will often be skewed or bimodal or just a big old mess. This happens even if the distribution of Y, at each value of X, is perfectly normal.

What to do instead:

Because normality depends on which Xs are in a model, check assumptions after you’ve chosen predictors

Conclusion:

The best rule in statistical analysis: always stop and think about your particular data analysis situation.

If you don’t understand or don’t have the experience to evaluate your situation, discuss it with someone who does. Investigate it. This is how you’ll learn.

 


Should Confidence Intervals or Tests of Significance be Used?

December 20th, 2019 by

What is a Confidence Interval?

Any sample-based findings used to generalize a population are subject to sampling error. In other words, sample statistics won’t exactly match the population parameters they estimate.

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The Wisdom of Asking Silly Statistics Questions

November 12th, 2019 by

I’ve written about this before–there is just something about statistics that makes people feel…well, not so smart.

This makes people v-e-r-y reluctant to ask questions.

This fact really struck me years and years ago.  Hit me hard.

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Eight Data Analysis Skills Every Analyst Needs

October 24th, 2019 by

It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard.

It’s true that more statistical knowledge is always helpful. But I’ve found that statistical knowledge is only part of the story.

Another key part is developing data analysis skills. These skills apply to all analyses. It doesn’t matter which statistical method or software you’re using. So even if you never need any statistical analysis harder than a t-test, developing these skills will make your job easier.

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The Secret to Importing Excel Spreadsheets into SAS

January 21st, 2019 by

My poor colleague was pulling her hair out in frustration today.

You know when you’re trying to do something quickly, and it’s supposed to be easy, only it’s not? And you try every solution you can think of and it still doesn’t work?

And even in the great age of the Internet, which is supposed to know all the things you don’t, you still can’t find the answer anywhere?

Cue hair-pulling.

Here’s what happened: She was trying to import an Excel spreadsheet into SAS, and it didn’t work.

Instead she got:

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