Stats can be very informative, but they also can be misleading.
When it comes to interpreting and analyzing statistical data, there are some common pitfalls that are easy to fall into, including:1.
Over-emphasizing the power of correlation to the results.
The idea that all statistical results can be directly compared to one another, without the influence of outliers, is a common one.
The problem with this approach is that when the power in the correlation is too high, we can easily end up with an “unstatistically significant” result.
When we’re talking about a single result, there is no way to see whether it has a statistical significance.2.
Using a lot of statistical methods.
A large number of statistical techniques can be used to produce a result that looks good.
However, it’s often the case that the statistical significance of the result lies in the relationship between the two variables.
The more statistical methods you use to produce the same result, the less likely you are to get a statistically significant result.3.
Not using a statistical model.
Many statistical methods, like regression, are based on assumptions that don’t necessarily apply to the data.
For example, if we think that the mean and standard deviation are close to one, then we may think that there is a strong relationship between them.
In reality, we would be surprised if there was no relationship.
For that reason, it is important to use an appropriate statistical model when analyzing your data.4.
Not properly considering the underlying assumptions of the model.
Statistical models are built to answer a specific question, and not to answer the question, “Is this model accurate?”
To get a reliable and accurate answer, you must make the assumptions that you have in mind when designing the model, and you should be able to apply those assumptions in real-world data sets.5.
Using too many statistical models.
If you’re looking to use statistical methods to analyze your data, you should consider whether there are any limitations to what statistical methods can do.
For instance, you can’t just randomly select an arbitrary number of variables, or you can only analyze data from a particular set of data.
This can lead to problems when it comes time to calculate statistical tests.
If we use a statistical approach for all our calculations, we’re going to miss the most important ones.6.
Using an invalid statistical model, when it’s actually relevant.
Sometimes statistical methods have limitations that are not relevant for the data we’re analyzing.
For this reason, we often make assumptions about the underlying data that can have a significant impact on the results of our analyses.7.
Using the wrong statistical model for the wrong purpose.
In some cases, statistical methods will be used when they are more appropriate for the situation.
For a case like this, you may want to avoid using statistical methods in the first place, and instead choose a different approach that will provide a better value.
For the most part, you’ll be able find a better way to analyze the data using a different statistical method, even if it’s not the one you’re used to.
For more on how to use statistics, check out the infographic below: