5 Incredibly Useful Lessons I Learned From Harvard Business School's New Online Course

This Post is for BEST COMMENTER CONTEST

I recently spent 11 weeks from April through July taking a new online course from Harvard Business School.

Called HBX CORe (Credential of Readiness), it's a "pre-MBA" course that tackles the basics of statistics, economics, and accounting using HBS's case study method and a proprietary user experience.

Its target demographic includes professionals who are looking to make up for their lack of a formal business education in hopes of advancing their careers. Business Insider covered the $1,500 price (which has since increased to $1,800).

After spending approximately 150 hours with it, I can say that I believe it is worth the financial and time investment, and is superior to free online courses in the same subjects.

HBS professors Jan Hammond, Bharat Anand, and V.G. Narayanan take students through the mechanics of their respective classes, but the case studies help teach practical lessons that can add another layer of understanding about how companies operate every day.

Here are some of the most useful lessons I learned:

The Winner's Curse means in many auctions, the winner may actually be the loser.

Unless everyone in an auction has perfect information about the value of the item up for grabs, the winner is just whoever happens to be the most aggressive or optimistic.

Someone who bid lower than the item's worth has a surplus of zero and doesn't win, and someone who bids exactly what the item is worth also has a surplus of zero and doesn't win. That means the person who wins overestimated the value and has a negative surplus.

Product bundles maximize value rather than creating waste.

You've probably bought several product bundles in your life where you felt like you were forced to buy something you really didn't want: You really needed Microsoft Word, could maybe use PowerPoint, and figured you'd maybe use Excel once or twice, but the bundle was a better deal than just buying Word and wondering if you should also pay full price for PowerPoint.

But the beauty of this classic Office bundle is that it maximizes revenue for Microsoft not because products are forced on you, but because there is a negative correlation among preferences of customers. There was someone whose preferences were similar to yours, except they really wanted Excel, moderately wanted Word, and barely wanted PowerPoint.

In the end, Microsoft wins by creating price discrimination, a strategy where similar goods are offered at different prices in different markets, like someone paying full price for paper towels and another purchasing paper towels only because they have a coupon.

Price ceilings and price floors can end up further complicating the problems they're meant to solve.

When the state of New York decided to enact an anti-price gouging law on gasoline in the wake of Hurricane Sandy, it was with the intention of keeping prices fair for New Yorkers of all walks of life.

Because the price was below the equilibrium price, in which supply equals demand, there was a shortage of gasoline and people had to spend hours in line for a shot at filling up. And because the price could not be raised, companies from other states had no incentive to ship gasoline to New York customers, resulting in some struggling citizens without gas.

A similar situation occurs when a minimum wage, an example of a price floor, is used in an elastic labor market — one in which workers would be willing to work more hours if paid a higher wage — and increases unemployment due to a surplus of labor.

A company's fixed costs are irrelevant to pricing decisions.

Fixed costs like rent and equipment expenses don't vary as the quantity produced of a product changes. Variable costs like raw materials and packaging do vary with quantity changes.

Fixed costs are relevant to whether or not a company should enter an industry or stay in business, but are irrelevant to pricing. If a coffee shop owner, for example, spent $300,000 setting up her business and produces a standard cup of coffee at a cost of $2, any value from a price above $2 is her profit, and she can lower the price all the way down to $2 if a price war with a competitor breaks out; the $300,000 is not a factor in determining how much the coffee should go for.

The Central Limit Theorem explains why we can confidently use random samples to learn about a population.

If you're measuring the impact of a variable on a population — say, how distance from downtown affects the price of a suburban home — the distribution of the averages of those samples will be normally distributed (i.e. it will be in the shape of a bell curve). The average of this resulting bell curve will be equal to the true average of the population.

In the real world, no one actually uses the huge amount of time and resources it would take to collect a bunch of samples and map their averages. Instead, we know that there is a 95% chance that the average of the sample is within two standard deviations of the population's average. Without getting into the math behind it, this is how we confidently determine ranges of values from a sample.

Read more: http://www.businessinsider.com/lessons-from-harvard-business-school-hbx-core-2015-8#ixzz3iqrGcdut