Research Glossary

When researchers say that they are “controlling for” something, what they mean is they are removing the influence of that thing (a variable) mathematically, in order to better assess the relationship between other variables.  For instance, in our study on B2B marketer compensation, we asked participants to name the salary increase it would take to […]

A/B testing is a method used in business-to-business (B2B) contexts to compare two versions of a webpage, email, advertisement, or other marketing asset to determine which one performs better in achieving a specific goal, such as increasing conversions or engagement. In A/B testing, the audience receiving the test should be randomly assigned to either Version […]

Comparing Averages of Two or More Groups As we describe in this blog, most statistics are about uncovering the reasons behind differences or variations among individuals, groups, events and the like. Analysis of Variance (ANOVA) is one of the most common methods of analyzing the sources of variability – hence its name. Similar to an […]

Every statistical test – in fact, every comparison of every kind – that we do is based on some assumptions. At base, we assume that the data we are analyzing is not fabricated and that there were not egregious errors in how the data was collected. Virtually all inferential statistics have some assumptions that have […]

The world is a very complex place. Even in the relatively controlled world of B2B, there are always a huge variety of factors that exert some sort of influence on whatever you might be interested in understanding. Many of the factors that influence how people and businesses behave are random and/or hidden from our view. […]

A bell curve is a graphical depiction of a normal distribution. Bell curves have distinct characteristics which enable predictions and inferences. The highest point of a bell curve is the average for the thing being measured. Things which fall into a normal distribution and can be shown as a bell curve obey the 68/95/99.7 rule. […]

Between subjects effects refer to how differences between subjects (people, teams, organizations, companies, etc.) affect how they behave or perform. Many of the differences we are interested in are like this. For example, how does web traffic received from two sets of accounts differ, where one set of accounts was exposed to advertising and the […]

Centering variables is a very simple, but very powerful research tool. All it involves is subtracting the average for a set of data from each value. If you have 10 data points, you would take the average for all 10, subtract that average from each data point. Where a value was above the average, you […]

Do different groups have different preferences or outcomes? The chi-square test is a statistical method used to determine if there’s a reliable relationship between two categorical groups. For instance, suppose we’re interested in whether adoption of the attribution measures marketing-sourced, marketing-influenced, or both differs depending on whether they practice Account-Based Marketing (ABM) or not. Because […]

When we measure a sample of anything, we end up with an exact number, typically an average of whatever we have measured (e.g., 4.5 opportunities produced per BDR per month). That number often seems very precise, and for the sample we measured, it is. But if we wanted to know what to expect going forward […]

Do two things tend to change together? Correlation analysis measures whether changes in one thing are reliably associated with changes in another. The easiest correlations to think about are those where one measurement increases, and then a second one also consistently increases.

A dependent variable is an outcome that you are measuring and is often referred to as the outcome variable. For instance, if you’re conducting an A/B test with two different subject lines (“Free Shipping Today!” versus “Limited Time Offer!”), the independent variable would be the different subject lines used in the emails. The dependent variable […]