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Five Tips to Avoid Sampling Bias in Alternative Data Interpretation

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Five Tips to Avoid Sampling Bias in Alternative Data Interpretation

Alternative data sources are extremely rich nowadays. Whether you collect your information from social media comments, sensors, or credit card transactions, for instance, you will most likely have access to (mostly) unlimited data. How do you know what to choose and how to avoid selecting the wrong data for your purposes? The next sections introduce you to sampling bias and how to avoid it, so you can accurately collect and interpret data.


What is Sampling Bias?

Sampling bias happens when your chosen data do not accurately represent the intended audience. For example, you decide to collect social media information to understand how seniors use your products or what they appreciate the most regarding your services. If you mistakenly collect information from all age groups, your results will not represent your intended audience anymore, since you have data collected from everyone.

Sampling bias usually happens without the researcher’s intention and occurs when some members of a population (i.e., youth) are more likely to be selected. In the example above, you may end up with more information related to young adults instead of elders if the social media channel selected by you has a larger portion of young users compared to seniors.


Five Tips to Avoid Sampling Bias

Choose a DaaS provider

Sampling bias is extremely common when you perform analysis on a random dataset or on available data. The best way to avoid this is to carefully put together your datasets, rather than collect all the information. The easiest method is to opt for a data provider, such as Coresignal, where you can order only the data you need, following your specific criteria.

Use Dynamic Datasets

In the fast-paced world nowadays, cognitive bias can easily occur. For example, if you choose what data to select or exclude, you may unintentionally create sampling bias. This happens if your chosen sample data is not representative of the population – thus, your data holds little value.

Related to the example above, choosing to focus on seniors when your actual audience is a mix of all age groups can lead to sampling bias. The results will only be true in the case of aging population, so your insights cannot be generalized to include other age groups. This could also happen if you decide to include all age groups and you end up with 70% of comments collected from young adults only.

The problem is that your customer base will never be equally divided among age groups. Also, depending on customer behaviour, trends, and releasing new products, your customer base can fluctuate significantly over time. This is why switching to dynamic datasets and using machine-learning models help you avoid sampling bias – artificial intelligence helps you to monitor and measure your data over time while updating it constantly.

Handling Irrelevant Data

When collecting information, it’s common to reach the conclusion that some of the data in your set are not relevant or the data set is incomplete. This is also a type of sampling bias; however, instead of removing such information, it’s best to try to understand why such data were included in your sets in the first place. This could tell you as much of a story as you’d interpret in your intended data.

Confirmation Bias

When conducting research, the most important step is to decide which information may serve your purpose. A common sampling bias is to choose only the data that support your hypothesis. For instance, if you run a market analysis that indicates that most of your customers belong to the +45 age group, you will not question the analysis if this was your own hypothesis in the beginning. However, if the results indicate that most social media users are 20-30 years old, you may be more likely to question the process, re-evaluate the analysis method, the data, or the algorithms, thinking that there has been a mistake.

Conflicting Data

Outlier bias is a different type of sampling bias. This happens when we mistakenly include skewed data in our samples. For example, you collect all the transaction data to see how much your customers spend on your products. However, if you have 99% retail consumers and 1% large corporations, including business transactions in the whole data sample would skew the results of your analysis.

To avoid this, you can use the median of your data to find out a more accurate representation of your data set. For instance, if most people spend $1,000 per month on your product or services, and two companies spend more than $10,000, the latter act as outliers and should be removed from your data sample to reach more accurate conclusions.


All in all, sampling bias is common when collecting data. This could happen for a variety of reasons – including your own assumptions, method of collection, and others. When using automated systems, such as machine-learning methods, and collecting accurate datasets, sampling bias can be eliminated using the tips above.

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