It is a capital mistake to theorize before you have all the evidence. It biases the judgment.

Sherlock Holmes never had “all” the evidence; usually he had just barely enough to arrest a villain that confessed or did himself in.  Case Solved.

If you really want a light introduction to statistics, try “Statistics for Dummies.”

The “science projects” that grade school and high school teachers assign to students are usually as authentic as the Baking Soda Volcano project.

Most teachers K-12-post doc are clueless about statistics or scientific methods.  What they teach about it is worse than ignorance, and twice as dangerous.

If your kid does get interested in science on some subject, there are lots of organizations that will work with them in various ways, such as 4-H or a nearby college.

“A man who acts as his own lawyer has a fool for a client.”

Rush Limbaugh is not quite as good as he says he is on explaining science, finances, unemployment insurance, etc., but he is dyn-o-mite with lefty politics and agendas.  And he does let you know about good sources of information on the other subjects, such as Roy Spencer on global warning.

Once your interest is piqued about a topic or issue, you can wander around Amazon, doing searches and following book recommendations.

Wikipedia may not be is not reliable, but it can suggest things to check out elsewhere on the net.

There are charlatans just about everywhere you look.  I understand that you are a lawyer, Book.  Have you ever heard of a shady lawyer?  I have.  John Edwards comes to mind.   I know there are shady scientists.  The pharmaceutical companies I have worked with have fraud detection policies in effect, and aggressively investigate any hint of fraud that rears its head.  Their corporate lifeblood depends on doing honest research only.
From what little I know of legal practice, I think a scientific hypothesis is to a scientist what a legal brief is to a lawyer.

Characterizations (observations,[41] definitions, and measurements of the subject of inquiry)
Hypotheses[42][43] (theoretical, hypothetical explanations of observations and measurements of the subject)[44]
Predictions (reasoning including logical deduction[45] from the hypothesis or theory)
Experiments[46] (tests of all of the above)”

 
“Characterization” is the act of noticing something that hints at a possible cause-effect relationship.  Growing plants under a variety of different conditions could very easily be part of that step.
 
However, it seems to me, if a teacher insists on a formal statement of a hypothesis, a student is perfectly justified in offering the null hypothesis:  “I hypothesize there will be no difference between the test group and the control group.”

Owen G’s point is an excellent one.  A bit of observation up front can help narrow the research substantially.  If your observations, or prior studies, have suggested that bacon might cause breast cancer, there is nothing wrong with a study to test that specific hypothesis, although the hypothesis would be stated much more objectively than you have it and the study would be objective as well.  The “null” hypothesis would be along the lines of “there is no relationship between the amount of bacon consumed and the frequency of breast cancer.”  That’s a perfectly legitimate question.

Book, I suspect that more than distrusting the method, you distrust the people who are using it.  But don’t blame the hammer for being a hammer.  Hammers are useful implements.  Blame the thug swinging it for breaking windows.  Or, in an analogy you will probably appreciate in light of your recent posts, don’t blame the gun, blame the shooter.

Don Quixote’s point is also a good one, which is that a wide open question becomes useless because it makes it impossible to assemble meaningful data.  I’d still prefer a narrow question (“Does what we eat affect breast cancer rates?”) to a careless and biased pre-answer (“Bacon is the culprit.”).

One thing DQ’s response made me realize is that I automatically assumed that, because we have computers, it’s much easier to assemble and, even more importantly, sort data.  In the old days, 1,000 women describing their every meal for a year would generate an insane amount of data.  Nowadays, though, you can have them fill out a daily diary on the computer, that data is automatically put into a spreadsheet and, with the press of a button, you can sort the information in as many ways as you can imagine — age, ethnicity, types of foods, etc.

The old hypothesis method, in a way, is predicated upon the great difficulty people used to have in making sense of lots of data.  That’s simply not the case anymore.  Nevertheless, whether you have narrow, hand-sorted data, or massive amounts of machine-sorted data, the primary rule is always going to be “garbage in, garbage out.”  You still have to ask intelligent questions.

I’m starting to ramble, so I’ll sign off here before I expose even more of my ignorance.