Answer: If you have an isolated piece of data, there is a near infinite number of hypotheses that can explain that piece of data. This is called “Underdetermination”.
Therefore, just going out and collecting bits of data doesn’t really advance our understanding of the universe. A near infinite number of possible explanations? We’re overwhelmed. Therefore, data without a hypothesis to relate it to can’t be evaluated.
BUT, one of the things you do when testing a hypothesis is to figure out what data you are looking for. Instead of collecting just any data, you are looking specifically for particular data.
If the hypothesis is true, then there is data that should be present if you look for it. For instance, say my hypothesis is that people who smoke have less lung capacity than non-smokers. What do you need lung capacity for? Well, blowing out birthday candles. Or blowing up a balloon. So a deduction is that non-smokers will not be able to blow up a balloon with one breath as a non-smoker. That is a deduction.
So now you can look for a specific fact: ability to blow up a balloon on a single breath.
Get a pack of balloons (spherical ones are best), and at least 20 people: 10 non-smokers and10 smokers. Then have each person take a deep breath and try to blow up a balloon. Measure the circumference. Have each person do it 3 times. Average the circumference measurements of all the tries of all the people in each group. Compare.
(My ENT has a fancy machine to measure lung capacity, but the balloon thing will work. I picked this because I saw a student use it once in a high school science fair. I thought it was pretty clever.)
If it turns out that the circumference is the same between the groups, you have managed to falsify the hypothesis. You at least know it is wrong.
What you also can do when you have a hypothesis and experiment is that you can design the controls to eliminate as many of those alternate hypotheses that we can think of. For instance, we would also expect younger people to have greater lung capacity than older people. And men are generally bigger, with bigger lungs, than women. So you could make sure you matched up ages and gender between the smokers and non-smokers so you can eliminate those other hypotheses.
So now the data means something — because it is related to a hypothesis. You don’t just have the circumference of balloons blown up by 20 random people. That could mean anything. Instead, those circumferences tell you something about the effects of smoking on lung capacity.
Explanation:
