Data has been considered one of the most important factors within a company, given the fact that it allows all kinds of analysis to be carried out. Basically, data has been established as a product of great importance when managing business strategies and management. To understand them, statistical significance is used.
But… What is statistical significance? It can easily be defined as the possibility that an analysis of such valuable data yields results that are not random. That is, that a reason for generating these results and the variables that cause them can be attributed.
Statistical significance turns out to be one of the measures for companies to carry out different procedures based on data collected from customers. Secondly, analyzing data has become a necessity, for their constant growth and the desire to find patterns to use them.
Which leads us to consider this means as a mathematical way to ensure that the results of a statistic are accurate. Defining in this way, what is the factor that generates said results obtained.
Importance of statistical significance
All companies use data to get to know their customers or target audience through them. What returns to statistical significance is one of the ways in which a company can know the repercussion of the data obtained from the user in the procedures.
And this leads to the high level of importance that this medium has, to support an analysis that is accurate. Since a statistic based on incorrect information can lead a company to make decisions that greatly harm investments. So it is essential to make use of statistical significance to make decisions based on high probabilities.
Situations when statistical significance is useful
Taking into account the level of importance that statistical significance has, it can be very useful in certain situations such as:
- Analyze conversions on a web page.
- Statistics of conversion rates and response to notifications or emails.
- User reaction to new product launches.
- User reaction to prices.
- User reaction to a new design on your website.
- User reaction to any new features that have been released.
Statistical significance considerations
There are certain characteristics that must be taken into account based on statistical significance, such as:
- The results of this type of test cannot always be used in companies: Why? It turns out that some results cannot be put into practice and it is essential to take this into account. It is also important to know that despite being significant, a result can still be random but a lower percentage.
- Some data may be falsely correlated: This establishes that the data may not have a cause that affirms its correlativity.
- The results obtained in the past do not affect the present or future: It is important to define that subsequent data and their results may not apply today. Which means that, when investing, the variables and correlations can change, influencing characteristics such as prices.
Types of Statistical Significance Tests
Depending on the type of research you want to carry out, there are several types of statistical significance tests that can be used. All this, to study data that help to affirm that a result is generated by a specific variable.
null hypothesis
Since the tests are based on a hypothesis, in this case we speak of the null hypothesis, it seeks to refute what the hypothesis in general wants to demonstrate. All the data have null hypotheses that allow to refute a statistical significance through their parameters.
In the most common cases, a parameter equal to zero is established, defining that a variable has no impact on the result studied. By managing to refute this hypothesis by at least 95%, it can be said that then the result is by statistical significance.
One of the examples that can be established is “eliminating offers would not reduce the number of purchases”, taking into account a null impact as a hypothesis. If proven, then it would be said that the result obtained from a series of data was not statistically significant.
Alternative hypothesis
Unlike the alternative hypothesis, the null hypothesis is the one that you want to test in order to obtain statistical significance. In this case we can establish as an example to say that “eliminating the offers would decrease the amount of purchases”.
This theory seeks a way to contradict what the null hypothesis raises, defining that the result obtained is due to a variable. Which is known in this case as the “elimination of offers”.
Basically, it seeks to establish two theories that define which is the most supported by the data in order to know if it has significance or not. In addition to that, it is important to note that rejecting a null hypothesis does not directly imply that the other hypothesis is being tested.
It is simply offered as a support. And it should even be recognized that no matter how significant a hypothesis may be, the impact need not be great. So it is possible to have results with statistical significance, but a very low impact.
How to calculate statistical significance?
Now we know the important role that hypotheses play. Since they could establish whether or not the proposed hypothesis affects these results through the variable, to obtain statistical significance.
Although many hypothesis testing methods are known, It is necessary to take into account certain definitions that help to better understand the results. Firstly, we have the normal distribution, which represents the way in which the data is distributed and is composed of:
- Half: Data average.
- Standard deviation: Extension of said data that defines the variation of the same in different values.
Thanks to the normal distribution, it is possible to know the location of a data point within standards based on the mean and the standard deviation. Due to this, it will be known if a point has an anomaly depending on the standard deviations compared to the statistical mean.
Now, the Z score is another of the definitions to take into account when performing a statistical significance test. Well, it is known as the distance between the data point and the statistical mean. To calculate it, it is necessary to subtract the distribution mean of the point to be analyzed and then divide it by the standard deviation.
It is also important to highlight that the normal distribution aims to know the significant level of a result. Which would establish that the higher the magnitude level of the Z score, the less likely that the result is by chance. Determining which is more significant.
Finally, it is essential to define P-values. It can be said that a P-value is the possibility of accessing results quite similar to those already measured by means of a correct null hypothesis. What it means is the percentage of probabilities that the results obtained are extreme.
Z-Test
In order to perform a Z test, one of the most widely used statistical significance tests, the normal distribution curve is needed as a reference. It is also important to obtain a Z score that allows you to carry out your study and finalize the P value. If the result is less than the level of significance, then the hypothesis is significant.
Its formula is Z = (x – μ) / (σ) and Z = (x – μ) / (σ / √n) to study a population; where:
- Z: Z value.
- X: Data point to consider.
- µ: Distribution mean.
- σ: Standard deviation.
- n: Population sample size.
You should keep in mind that statistical significance tests are quite accessible tools that allow you to validate an analysis. But, They will not always be completely accurate, so it is necessary to obtain true data to help obtain accurate results.
It should be noted that an error in the data could cause a result that is not in accordance with the test and cause repercussions. In addition to that it is essential to carry out this test in the safest way possible, to avoid calculation errors and to have an adequate level of significance.
In case of having results and not being completely sure, the test can be repeated to ensure that there are no errors in the P-values. Since this is where there can be a greater margin of error and it can cause an analysis based on false positives.
The best of all is to consider that this tool allows companies to carry out different commercial procedures. Considering that they provide a clear analysis of a set of data to know if variables could have an impact on said analysis. Nevertheless, It is always essential to make sure that you avoid making decisions that could harm your company.
At we use statistical significance to perform CRO functions. Do you want to check what works best in your company? Get in touch with us and we will tell you more about statistical significance and its uses in
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