More is better, right? Wrong. In The Paradox of Choice: Why More is Less, Barry Schwartz explains how too many options actually cause more psychological distress. And nowhere is the overabundance of choice more prevalent than the Internet, where any given website can present us with an overwhelming number of alternatives at once.

The solution is not carrying fewer products or content. One of the beauties of the online world is that there's something for everyone. As Chris Anderson explains in his popular book, The Long Tail: Why the Future of Business Is Selling Less of More, "You can find everything out here in the Long Tail." It is within this plethora of options where the content and products that truly meet people's needs are found, and where companies ultimately make more money.

If carrying less is not the answer, what is? The online world has devised numerous strategies in an attempt to guide users to products and content that will best meet their needs. Many large sites employ the efforts of skilled merchandisers or editors armed with aggregated analytics data to help point the way, and others rely on crowd-sourcing techniques such as ratings and reviews to narrow down the choices.

While the above methods can be valuable in navigating the quagmire of choice, they all suffer from one major problem: bias. Bias comes in a number of guises, and we will walk through seven of the most common and detrimental here. In the end there is hope, though, as there are new technologies capable of largely evading these biases.

Personal Bias
The human brain fundamentally approaches the world in a self-centered way; we see the world through the filter of our past experiences and knowledge as well as our own interests and attitudes. But since we do not have access to all of the information that drives another person's attitudes and behaviors, we are often wrong when we attempt to predict what another person or group of people will find interest in.

One way to mitigate the problem is to run a focus group. Though this can help remove the biases of the expert, members of a focus group suffer from personal bias as well, such as when a dominant opinion influences the entire group. In addition, you will never have a truly representative sample of people and so will be swayed by the luck of the draw on the attitudes of the specific people you have chosen. The bottom line is, whenever you use a small sample of the population - be it an expert or a focus group - to predict the greater population, you need to recognize the influence of personal bias.

Squeaky Wheel Bias
Crowd-sourcing, such as ratings and reviews, has become a popular technique for creating recommendations online. In theory, this approach has few flaws: if every single person who came to the site weighed in with their opinion on every product, you would get a perfect representation of consumer attitudes.

The problem, of course, is that not everyone contributes. The tendency is for certain kinds of people to make their voices heard, particularly when effort is involved (such as in a review).

The most vocal and misleading group of contributors is what I like to call the "squeaky wheels," for example, those people who simply like to complain. But it can also be any one of us when we have a negative experience. Negative experiences tend to stand out more than positive ones and also encourage us to take action.

Overly positive reviews happen too, but again what happens is that you get a representation of the community that is biased to the two extremes - five scathing reviews, three glowing ones, a few people who just like to be heard. At the end of the day, 99% of the population remains unspoken for.

Contextual Bias
Another challenge with relying on explicit user feedback is contextual bias. Imagine, for example, reviews of a digital camera. One person might say that the "resolution is incredible" and another that "resolution sucks." They may both be right. Perhaps one person uploads pictures to the web, where the resolution is great, and another likes to print them out where higher resolution is needed. The two reviewers are using the camera and rating it from two different contexts. Ratings have no way of reflecting such nuances and reviews only do if the person writing it is aware of their contextual bias.