In office, we have some really slow software application. In order to show that the waiting times over the whole departement incredibly sum up and lead to high costs, I needed an objective measurement method. First, I got a manual stopwatch which allowed to track how long I had waited. However, I was not able to answer the question: “How long do I work with this program in office routine?” There are not so many tools out there and most of them were a little bit over the top. That is why I developed a small utility for monitoring software usage over time: Software Usage Recorder! more →
Fossil fuels will vanish in the future – for sure! What we don’t know is the exact point in time when this is going to happen. Based on the BP Statistical Review of World Energy 2016, we find that fossil fuels will last up to 100 years. Proven oil reserves, which will last for the next 50 years, are expected. However, BP has an interest in keeping the energy reserve numbers great: Any other message would make the oil price jump up and renewable energy would become less unattractive. Especially when considering the rising demand for energy (e. g. in China) we need to prepare for earlier shortages. But what does it take to substitute fossil energy sources by renewable ones?
World energy consumption in 2014 mainly adopted from the simplified energy balance sheet table of the IEA; an asterisk (*) indicates that the data was not fine-grained enough to guarantee full accuracy
Not only do the numbers reflect an incredible thirst for energy, but it also shows to which extent we damage earth by exploiting these resources. The bottom line is that this energy supply from coal, oil, natural gas and nuclear energy (~107,000 TWh) must be substituted. What are the options?
I like white sneakers and I don’t want them to become dirty. That’s why I always check on the next day’s precipitation probability. If I intend to leave only for a couple hours, then the breakdown of the rain risk is of particular interest to me. When comparing the probabilities on hourly and daily basis I could rarely figure out the relationship between both. Consider the following weather forecast:
Fictional weather forecast on daily and 6-hours basis
For simplicity I chose to present the rain risk for periods of 6 hours length. Anyway, in what way are the daily rain risk of 40% and its breakdown of 20% over 12 hours related? Can we trust the weather forecast’s statements anyway? This article will discuss the topic.
Those of you who have read previous articles of mine already know that I like tricky questions – especially if they don’t look too complicated in the first place. Here’s one for you to try: Does the penny make a half or a full rotation when rolling it around another penny?
Would you expect Mr Lincoln to show his head upside down?
It was really surprising for me when I thought about this kind of operation, namely division by arithmetic means and expected values. People tend to work with means and expected values very intuitively. You can add and multiply them without any issues. Dividing on the other hand can be misleading and I am going to illustrate this with some neat examples.
Imagine that a friend of yours would like to play a game. Your friend writes down two different numbers on two separate slips which you cannot spot. Afterwards you are allowed to choose one of the slips and read the number written on it. The game’s goal is to make a rough guess on the value of the second number.
You think there is a 50:50 chance for guessing correctly? Although you may not believe it: The probability for a success is definitely higher when you apply the following strategy! more →
I guess that you already know a little bit about hypothesis testing. For instance, you might have carried out tests in which you tried to reject the hypothesis that your sample comes from a population with a hypthesized mean µ. As you know that the sample mean follows a t-distribution (or the normal distribution in case of huge samples), you can define a rejection region based on a specific significance level α. In a one-sided test, sample means which are less than a critical value CV might be considered to be rather unlikely. If the obtained sample’s mean falls into this region, the hypothesis gets rejected at this particular signficance level α.
Distribution of sample mean and rejection region for one-sided test
We know the chance of rejecting the hypothesis although it’s true (Type I error), because it is the chance of obtaining just one of those values from the rejection region (plotted red). But how likely are we to reject a hypothesis if it’s indeed false? In other words: How small is the type II error? more →