what is the null hypothesis and can you give me some simple examples?
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More or less, the null hypothesis is a hypothesis that states there wasn’t anything important discovered in observation. If it’s a two-group trial and control study, the null hypothesis is generally “the trial group is no different”.
If the study is testing a medication, the null hypothesis is “it doesn’t do anything”.
If the study is comparing gender differences in some mental task, the null hypothesis is “there isn’t a difference”.
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The null hypothesis is the hypothesis that an observed result (e.g. a difference in average weight between a group of rats fed a steady diet of Twinkies and another group fed normal rat chow, or the correlation between sunspot activity and the Metacritic ratings of Taylor Swift albums) is entirely attributable to random chance.
You can use something called the normal curve, also called the Gaussian distribution, to calculate the probability of said result occurring under conditions where there is nothing happening besides random variation.
By convention, if the probability is 5% or less – in other words, if you would have a 5% or lower probability of getting the same result under pure random-chance conditions – we reject the null hypothesis and say that the result is “statistically significant.”
The phrase “null hypothesis” is frequently misused by hacky pseudointellectuals to mean something like “the mundane as opposed to the extraordinary explanation” or “the negation of the hypothesis in question.” It means neither of these things – someone who uses the term in this way is just throwing around science-y phrases to sound smarter than they actually are.
The null hypothesis is simply “there’s nothing special about what I’m looking at”.
Imagine you have a coin. You can ask two very similar questions: “is this coin biased?” Or “is this coin balanced?”
When you ask “is this coin biased?” You’re effectively saying “I expect coins in general to be balanced, but think this particular one might not be”. When you ask “is this coin balanced?” You’re sort of saying the opposite: “I expect this sort of coin to have a 51:49 split between heads and tails, but this one might be perfectly balanced”.
In the first case, your null hypothesis is that the coin is balanced and has perfect 50:50 odds. In the second case, your null hypothesis is that the coin has a slight 51:49 bias.
The reason this matters is that the null hypothesis is what you go back to if the experiment is inconclusive. If you throw the coin twenty times and you get an 11:9 split, that’s nowhere near enough to justify changing your mind no matter what you believed in to begin with.
The null hypothesis exists to keep you objective when you design your experiment. Humans have a tendency to try to prove their beliefs correct, so you protect yourself against bias by using a null hypothesis, setting out to prove that nothing will happen rather than trying to prove that *something* will happen. An example is, “There will be no significant difference between boys’ and girls’ scores on an IQ test. Any difference will be due to chance.” That second sentence is important, most assessors will take points off if you don’t include it.
You may have question about the world around you. Does drinking beer cause you to have a beer belly?
You can design a study to determine Truth in the Universe. Randomly select 100 volunteers for your study; 50 will be in group A and will eat a specific diet and have a specific exercise regimen. You will choose some measure of “beer belly,” and measure it before and after your study period, say 6 weeks. A common measure for this is hip to waist ratio.
50 will be in group B, and will have the same diet and exercise regimen as group A, except some of the calories in group A’s diet will be removed and replaced with, say, 3 cans of Miller Genuine Draft each day, and this will go on for 6 weeks.
You can then compare how much the average hip to waist (HtW) ratio changes in group A, and compare it to the changes in group B.
***The null hypothesis states that there will be no difference in the change of HtW between the two groups***, and you will choose an appropriate statistical test to analyze this hypothesis. If there IS a difference in the change of HtW ratio in the two groups, your statistical test will tell you if the difference is statistically significant. If it is, then you get to decide if the difference is clinically significant.
Spoiler alert; the studies that have been done to examine this question have not been randomized double-blind controlled trials, and the answer is generally that beer consumption is not particularly associated with the presence or absence of a beer belly once you control for exercise and diet…
Colloquially, the null hypothesis establishes that no two things are correlated until they are demonstrated to be correlated.
It’s a way to start logic from zero, without bringing along presumptions from “common sense,” generalizations, genuine misunderstanding, pseudoscientific belief, or sociopolitical/religious bias.
Before a criminal trial, the null hypothesis is that the defendant is “not guilty,” and it is the prosecutor’s obligation to demonstrate that the defendant is correlated with the circumstances of the crime, thus “disproving the null.”
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