Well, it’s that time of year again!
Regardless of which holiday you celebrate and even if you celebrate the holiday of “I deserve a treat today”, you’re sure to find a statistics gift for yourself or your loved ones below. Just click on the image to go to the website and order. Go! Quickly before they run out! Shirts, cups, hats, toddler toys, and more, they’re all here.
Welcome to Really Simple Statistics (RSS). There are lots of places online where you can ponder over the minute details of complicated equations but very few places that make statistics understandable to everyone. I won’t explain exceptions to the rule or special cases here. Let’s just get comfortable with the fundamentals.
What are tails?
No, not these tails.
The tails in statistics refer to the predictions we make about our research results and how we want to hedge our bets.
One tailed tests
One tailed tests are what you use when you have a specific guess. Men are taller than women. Women like chocolate more than men like chocolate. Roses smell nicer than tulips. It’s like putting all your eggs in one basket. Take a guess and hold yourself to it.
Ideally, this is what you should be aiming for. You should have a prediction about what is going to happen before you conduct your research. You should do your homework and not just willy nilly see ‘what comes up significant.’
One tailed tests are advantageous because they give you a better chance of generating differences that turn out to be statistically significant, as long as, of course, your prediction turned out to be right.
Two tailed tests
Two tailed tests are used when you can’t make a guess. Will men or women eat more bread? Is basketball or soccer more fun? Do people spend more on coffee or on hot chocolate? In this case, you’re putting splitting your eggs between two baskets – maybe men but maybe women, maybe basketball but maybe soccer.
jdurham from morguefile
In reality, most of what we do in market research is based on two tailed tests. We don’t spend the time to develop specific hypotheses ahead of time. We wait to get the datatables and then search through hundreds of pages looking for whatever happens to be significant.
And that’s it! Really Simple Statistics!
The normal curve is an enigma for many people. We speak of good luck and bad luck, hope that we always have good and then curse when it turns out bad. Like when Cinnabon is closed on the same day you forgot to eat breakfast.
So far, Paul the Octopus has had a lot of good luck in predicting World Cup match winners. Perhaps he always goes for the food that is closest to him or the food that is in the best light or the food that moved most live-like or the food next to his preferred tentacle. I’m assuming, of course, that like humans who prefer left or right, Paul too has his own tentacle preferences. I’m also assuming that he isn’t juiced up or taking bribes.
Wouldn’t it be great fun if someone could collect up all the relevant variables and run some predictive modeling? Time of day, day of week, feeding schedule, lightness, location, direction, colour, and who knows what other selection criteria are of supreme importance to our eight legged friend. What kind of r square do you think we would get? 0.3? 0.8? Woah… too far into geeky stats there.
As fun as it is to listen to the Oracle of Paul, he won’t defy the odds. He’ll just take his rightful place on the normal distribution whether it’s on the extreme right or just slightly to the left of right. But I know we’re all hoping for the extreme right.
In Paul we trust.
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