Null hypothesis

Going to start off with some relevant definitions first Dependent variable — the thing that’s being measured, a response variable (ie. heart rate) Independent variable — the thing that’s being manipulated or set by the researchers; the variable that is hypothesized to cause a change in the dependent variable (ie. a drug treatment) Population — the group being tested. Important to note that the conclusion can only be generalized to the population of the study. If the experiment (“sample population”) only includes men of Asian descent aged 45 and over, then the conclusion cannot be assumed to extend to men of other backgrounds, women, young men, children, etc. The alternative hypothesis is the research question in the form of a true/false statement (This drug affects heart rate). The null hypothesis is the “blank.” It assumes there is no relation between the independent and dependent variables (This drug has no effect on heart rate). With no evidence, we default that the null hypothesis is true. The experiment aims to disprove the null hypothesis in favor of the alternative.* The p-value is the probability of getting the observed result under the assumption that the null hypothesis is true — that there is no relation between the variables. A small p-value means that it would be very unlikely to observe this result by random chance; therefore, it is likely that something is causing it. In a well-designed experiment, the cause can be attributed to the independent variable. *Just because a result is not significant, does not necessarily mean that the null hypothesis is definitively true, it just means we did not find evidence to say otherwise. Same goes for the alternative. Just because a result is significant, does not mean it is the end-all explanation. We just have evidence to support the conclusion. That’s not a go-ahead for all you conspiracy theorists out there to say “Gotcha!” If the results can be observed time and time again, then that’s more and more evidence to support the explanation. More on reddit.com

I understand the basic premise of hypothesis testing. I under stand why we fail to reject or reject the null hypothesis. I can't wrap my head around… More on reddit.com

The null hypothesis says "there is nothing going on here". Let's say you rolled a die three times and it always came up six. You conclude the die is probably loaded, and that becomes your hypothesis. The null hypothesis is the die is not loaded, and your results were due to chance or experimental error. After all, there is a 1 in 216 chance you would expect to see that on a fair die. You would next design an experiment to drive the likelihood of the null hypothesis so low that it can be ignored. If you rolled the dice ten times and got all sixes, that would represent a 1 in 60 million longshot, and strong evidence the null hypothesis is not true. Note that evidence against the null hypothesis is evidence something is going on, but not necessarily evidence for your hypothesis. Your hypothesis could have just as easily been "I am controlling the die with my telekineses", and eliminating the null hypothesis certainly would not support that conclusion. Further experiments would be required to determine exactly when the die always rolls sixes. The null hypothesis is often tied to the placebo effect. When people receive treatment, they tend to report improvement even when there is none. The null hypothesis remains "there is nothing going on here", and that any results were due to chance and/or the placebo effect. More on reddit.com

The null hypothesis assumes that there is no relationship between the variables you want to test and it exists for the sake of demonstrating statistical significance of data.

Lets say you have a hypothesis that X happens when you do Y. You preform an experiment where you do Y, and lo and behold X happens. The evidence from your experiment supports your hypothesis, but how do you know it wasn't a fluke? Maybe X and Y have nothing to do with one another, and they just happened to coincide this time?

This is where the null hypothesis comes in. It assumes that X is just as likely to happen, regardless of whether or not you do Y. A central task in proving your hypothesis is rejecting the null hypothesis. This is done through statistical analysis of your data to determine the likelihood of obtaining your results should the null hypothesis be true.

If that likelihood is low enough (usually <1-5%), you can say that your results are "statistically significant," or in other words your results are not a fluke and are therefore indicative of the relationship between X and Y.

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