Inferential Statistics play a crucial role in quantitative analysis by allowing researchers to draw conclusions and make inferences about a population based on a sample.
If you’re new to quantitative data analysis, one of the many terms you’re likely to hear being thrown around is inferential statistics. In this post, we’ll provide an introduction to inferential stats, using straightforward language and loads of examples.
Inferential Statistics play a crucial role in quantitative analysis by allowing researchers to draw conclusions and make inferences about a population based on a sample. They help to determine the significance of relationships between variables, test hypotheses, and make predictions. Without inferential statistics, quantitative analysis would be limited to descriptive statistics, providing only a summary of the data without any generalizations or inferences.
At the simplest level, inferential statistics allow you to test whether the patterns you observe in a sample are likely to be present in the population – or whether they’re just a product of chance.
In stats-speak, this “Is it real or just by chance?” assessment is known as statistical significance. We won’t go down that rabbit hole in this post, but this ability to assess statistical significance means that inferential statistics can be used to test hypotheses and in some cases, they can even be used to make predictions.
That probably sounds rather conceptual – let’s look at a practical example.
Let’s say you surveyed 100 people (this would be your sample) in a specific city about their favourite type of food. Reviewing the data, you found that 70 people selected pizza (i.e., 70% of the sample). You could then use inferential statistics to test whether that number is just due to chance, or whether it is likely representative of preferences across the entire city (this would be your population).
At this point, you might be wondering how inferential differ from descriptive statistics. At the simplest level, descriptive statistics summarize and organize the data you already have (your sample), making it easier to understand.
Inferential statistics, on the other hand, allow you to use your sample data to assess whether the patterns contained within it are likely to be present in the broader population, and potentially, to make predictions about that population.
It’s example time again…
Let’s imagine you’re undertaking a study that explores shoe brand preferences among men and women. If you just wanted to identify the proportions of those who prefer different brands, you’d only require descriptive statistics.
However, if you wanted to assess whether those proportions differ between genders in the broader population (and that the difference is not just down to chance), you’d need to utilize inferential statistics.
In short, descriptive statistics describe your sample, while inferential statistics help you understand whether the patterns in your sample are likely to reflect within the population.
Now that we’ve defined inferential statistics and explained how it differs from descriptive statistics, let’s have a look at some of the most common tests within the inferential realm. It’s worth highlighting upfront that there are many different types of inferential tests and this is most certainly not a comprehensive list – just an introductory list to get you started.
A t-test is a way to compare the means (averages) of two groups to see if they are meaningfully different, or if the difference is just by chance. In other words, to assess whether the difference is statistically significant. This is important because comparing two means side-by-side can be very misleading if one has a high variance and the other doesn’t.
As an example, you might use a t-test to see if there’s a statistically significant difference between the exam scores of two mathematics classes taught by different teachers. This might then lead you to infer that one teacher’s teaching method is more effective than the other.
It’s worth noting that there are a few different types of t-tests. In this example, we’re referring to the independent t-test, which compares the means of two groups, as opposed to the mean of one group at different times (i.e., a paired t-test). Each of these tests has its own set of assumptions and requirements, as do all of the tests we’ll discuss here – but we’ll save assumptions for another post!
While a t-test compares the means of just two groups, an ANOVA (which stands for Analysis of Variance) can compare the means of more than two groups at once. Again, this helps you assess whether the differences in the means are statistically significant or simply a product of chance.
For example, if you want to know whether students’ test scores vary based on the type of school they attend – public, private, or homeschool – you could use ANOVA to compare the average standardized test scores of the three groups.
Similarly, you could use ANOVA to compare the average sales of a product across multiple stores. Based on this data, you could make an inference as to whether location is related to (affects) sales.
In these examples, we’re specifically referring to what’s called a one-way ANOVA, but as always, there are multiple types of ANOVAs for different applications. So, be sure to do your research before opting for any specific test.
While t-tests and ANOVAs test for differences in the means across groups, the Chi-square test is used to see if there’s a difference in the proportions of various categories. In statistics, the Chi-square test assesses whether there’s a statistically significant relationship between two categorical variables (i.e., nominal or ordinal data).
As an example, you could use a Chi-square test to check if there’s a link between gender (e.g., male and female) and preference for a certain category of car (e.g., sedans or SUVs). Similarly, you could use this type of test to see if there’s a relationship between the type of breakfast people eat (cereal, toast, or nothing) and their university major (business, maths or engineering).
Correlation analysis looks at the relationship between two numerical variables (like height or weight) to assess whether they “move together” in some way. In stats-speak, correlation assesses whether a statistically significant relationship exists between two variables that are interval or ratio in nature.
For example, you might find a correlation between hours spent studying and exam scores. This would suggest that generally, the more hours people spend studying, the higher their scores are likely to be.
Similarly, a correlation analysis may reveal a negative relationship between time spent watching TV and physical fitness (represented by VO2 max levels), where the more time spent in front of the television, the lower the physical fitness level.
When running a correlation analysis, you’ll be presented with a correlation coefficient (also known as an r-value), which is a number between -1 and 1. A value close to 1 means that the two variables move in the same direction, while a number close to -1 means that they move in opposite directions. A correlation value of zero means there’s no clear relationship between the two variables.
What’s important to highlight here is that while correlation analysis can help you understand how two variables are related, it doesn’t prove that one causes the other. As the adage goes, correlation is not causation.
While correlation allows you to see whether there’s a relationship between two numerical variables, regression takes it a step further by allowing you to make predictions about the value of one variable (called the dependent variable) based on the value of one or more other variables (called the independent variables).
For example, you could use regression analysis to predict house prices based on the number of bedrooms, location, and age of the house. The analysis would give you an equation that lets you plug in these factors to estimate a house’s price. Similarly, you could potentially use regression analysis to predict a person’s weight based on their height, age, and daily calorie intake.
It’s worth noting that in these examples, we’ve been talking about multiple regression, as there are multiple independent variables. While this is a popular form of regression, there are many others, including simple linear, logistic and multivariate. As always, be sure to do your research before selecting a specific statistical test.
As with correlation, keep in mind that regression analysis alone doesn’t prove causation. While it can show that variables are related and help you make predictions, it can’t prove that one variable causes another to change. Other factors that you haven’t included in your model could be influencing the results. To establish causation, you’d typically need a very specific research design that allows you to control all (or at least most) variables.
We’ve covered quite a bit of ground. Here’s a quick recap of the key takeaways:
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