Hypothesis testing is one of the key method of inferential statistics.
It is based on the idea that we can define and describe a population based on the random samples we have collected.
So, if you want to define hypothesis:
What is Hypothesis?
It is an assumption about a population based on the sampling, which may or may not be true.
There are two types of statistical hypothesis:
- Null Hypothesis (H0) – Which is hypothesis, defining there is no effect of your treatment or test.
- Alternative Hypothesis (H1) – There is observable effect of the test or treatments on the population.
Point to remember: Hypothesis is always about the population parameter not the sample values or statistics.
For doing hypothesis testing there are 5 steps:
- Define your hypothesis for the experiment, the null hypothesis and the alternative hypothesis.
- Define the level of significance (the alpha value; in statistics which is often taken as 0.05; but based on your experiment you can determine your level of significance or alpha value. Based on which you can reject or accept your hypothesis)
- Your sample (Take random sample from your population and determine the statistical test based on the type and distribution of your sample)
- Decide (Use the p-value to accept or reject your hypothesis)
So, lets say we want to find out the effect of manure on maize yield. So, we planted maize across our state at 5 different plots (replication) and applied our treatment or manure to see the effect. So, for this we set up two hypothesis :
- Null hypothesis: there is no effect of manure application on maize yield.
- Alternative hypothesis will be : there is effect of manure on maize yield.
So, at the end of the season we harvested maize from our five plots and determine mean population and used t-test to determine the p-value and we found our p-value is 0.03.
So, now what it means?
As we have taken our level of significance (alpha value) as 0.05 and our p-value is less than the level of significance so we can reject the null hypothesis and accept the alternative hypothesis. And, it means there is a significant effect of manuring on maize yield or the yield has increased after the application of manure.
Lets say we got our p-value 0.08, then what does that means ?
Now, the p-value is greater than the level of significance or alpha value, thus we can not reject the null hypothesis and it means there is no significant effect of manuring on maize yield.
So, when we describe hypothesis, we should also know about the type-1 and type-2 errors.
What is type-1 error ?
Type-1 error is also known as false positive, it happens when null hypothesis is true but rejected.
What is type-2 error?
Type-2 error happens when the null hypothesis is false and you fails to reject it.
We will further discuss about type-1 error and type-2 error in next blog post.