What Is A Null And Alternative Hypothesis?

When carrying out statistical hypothesis testing, there are two rival hypotheses (or assertions) known as the null hypothesis and the alternative hypothesis. They may be used when testing different ideas and assertions when carrying out scientific research or testing in order to find an accurate outcome based on facts.
The null hypothesis will generally correspond to a default or general assertion when carrying out testing. When carrying out tests, every single assertion needs to be falsified against a number of observed data pieces. This is done by collecting together a series of possible outcomes or a set of data and then measuring how probable each of which is true.  For example, you could say that when looking at two phenomena that can be measured, the null hypothesis is that there is no relationship between the two.  If you are looking at how a drug responds to a specific disease, the null hypothesis is that the drug has had no effect at all.
The alternative hypothesis may be described as the negation or opposite of the null hypothesis. In the above examples, an alternative hypothesis would be that there is a relationship between the two phenomena that can be measured. Likewise, the drug will have an effect on treating the disease.
These two hypotheses are then compared using statistical testing. During this testing, the scientist will not try to prove their alternative hypothesis is true, they will try to prove the null hypothesis wrong. This is where the name ‘null’ comes from as the testing may help ‘nullify’ the null hypothesis.
The null hypothesis is a term that was invented by an English geneticist and statistician called Ronald Fisher. Although this term is generally paired with the alternative hypothesis, this was not actually developed by Fisher and was actually introduced by Jerzy Neyman and Egon Pearson.

The null hypotheses are a proposition that states definitive, exact relationship between two variables. That is it states that the populations correlation between two variables is equal to zero that the difference in the means of two groups in the population is equal to zero. In general the null statement is expressed as no relationship between two variables or no difference between two groups. The alternate hypothesis which is the opposite of the null is a statement expressing a relationship between two variables or indicating differences between groups.

To explain it further in setting up the null hypothesis we are stating that there is no difference between what we might find in the population characteristics the total group we are interested in knowing something and the sample we are studying. Since we do not know the true the true state of affairs in the population, all we can do is to draw inferences based on what we find in our sample. What we imply through the null hypothesis is that any differences found between two sample groups or any relationship found between two variables based on our sample is simply due to random sampling fluctuations and not due to any differences.

The null hypothesis has no relationship between two measured phenomenon where as the alternative hypothesis has the relationship between two measured phenomenon.

Anything with an equal sign on the symbol is the null (Ho) e.g. : = ,less than or equal to, more than or equal to.

Alternative (H1) is the opposite fr. The statement above. E.g. : Not equal to, .

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katherizna

Is a statement contradicting a theory poised by a research.This theory is commonly referred to as alternative hypothesis or a researchers hypothesis.A null hypothesis is normally formulated by a statistician in the negative direction with a sole aim of rejecting it.

If tossing a coin for ten times and five times head comes and five times tails comes then accept null hypothesis and in the same way if 8 times head comes and 2 times tails comes then accept alternative hypothesis and reject the null hypothesis.

Null hypothesis is that For example there are two group A and B and both perform a test on same data and if they result zero then it is Null hypothesis, on the other hand if there is a difference it is alternative hypothesis.

Null  hypothesis is when the difference of two samples is zero while alternative hypothesis there is a difference

Null and alternative hypotheses
The logic of traditional hypothesis testing requires that we set up two competing statements or hypotheses referred to as the null hypothesis and the alternative hypothesis. These hypotheses are mutually exclusive and exhaustive.

Ho: The finding occurred by chance

H1: The finding did not occur by chance

The null hypothesis is then assumed to be true unless we find evidence to the contrary. If we find that the evidence is just too unlikely given the null hypothesis, we assume the alternative hypothesis is more likely to be correct. In "traditional statistics" a probability of something occurring of less than .05 (= 5% = 1 chance in 20) is conventionally considered "unlikely".