Inferential Statistics

Have you ever read a statement like, “2.6 million Europeans are now vegan” and wondered, Wow, how did they find the time to ask everyone in Europe about their dietary choices?¹ Well, they likely didn’t. Instead, almost all research relies on some level of inferential statistics, a method where researchers take a representative sample from the population they want to study and draw conclusions or make predictions based on that data.

When we’re first introduced to statistics, we usually learn about descriptive statistics, which summarize the main features of a data set. Descriptive statistics report characteristics of your data like the distribution concerns, the frequency of each value, the central tendency and averages, or the variability. In this method, there is usually less uncertainty since the statistics aim to directly describe the sample, without assuming anything beyond the data at hand. For example, if you counted the number of chocolate chips in every cookie in one batch, you could use descriptive statistics to get a quick overview of all the cookies in that batch—for instance, the average number of chocolate chips each cookie has. But keep in mind that the data you collect about this batch won’t necessarily help you make any inferences about another batch of cookies.  

However, descriptive statistics isn’t always the best option. After all, sometimes you can only acquire data from smaller samples because it is too difficult, expensive, or outright impossible to collect data from the entire population that you’re interested in. For example, what if one recipe made 250 cookies? That would be a lot of cookies to eat in one day. Instead, inferential statistics allows us to make inferences using just a sample from an entire population. Using this method, you would only need to take a sample from just a few of those cookies, collect data on those, and then extrapolate (or infer information) to learn more about the rest of the cookies in that batch. As you can see, this approach is incredibly helpful when studying larger groups of objects or people, as it’s often far too time and resource-intensive to take data from an entire population. 

With inferential statistics, it’s important to use random and unbiased sampling methods. If your sample isn’t representative of your population, then you can’t make valid statistical inferences to generalize about the rest of the population. For example, if you only choose the best-looking cookies to test, they may be bigger or have more chocolate chips than the rest of the batch—which would skew your data on what the average cookie from that recipe truly looks like. Instead, you’d want to make sure the cookies you tested were randomly selected. The same is true for any other use of inferential statistics; researchers often use software programs to ensure the sample they study is truly chosen at random in order to limit bias.

Beyond the realm of baking, inferential statistics has helped us make advances in countless fields. In areas like science, economics, and medicine, it’s allowed for informed, data-driven decision-making. Inferential statistics is especially key in designing experiments and analyzing the results, as these methods are applied by scientists to determine if their findings are statistically significant and can be generalized to a broader population. Businesses also use inferential statistics for market research, customer behavior analysis, and forecasting trends, improving strategic planning and targeting. Lastly, epidemiologists and healthcare researchers use inferential statistics when understanding relationships between treatments and outcomes, estimating population health trends, and working with policymakers to guide public health interventions.²