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Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. Hypothesis testing can be defined as a statistical tool that is used to identify if the results of an experiment are meaningful or not. It involves setting up a null hypothesis and an alternative hypothesis. These two hypotheses will always be mutually exclusive. We can accept or reject the null hypothesis. We never say that the alternative hypothesis is accepted. An example of hypothesis testing is setting up a test to check if a new medicine works on a disease more efficiently.

## Null Hypothesis

The null hypothesis is a concise mathematical statement that is used to indicate that there is no difference between the two possibilities. In other words, there is no difference between certain characteristics of data. This hypothesis assumes that the outcomes of an experiment are based on chance alone. It is denoted as H0H0. Hypothesis testing is used to conclude if the null hypothesis can be rejected or not. Suppose an experiment is conducted to check if girls are shorter than boys at the age of 5. The null hypothesis will say that they are the same height.

## Alternative Hypothesis

The alternative hypothesis is an alternative to the null hypothesis. It is used to show that the observations of an experiment are due to some real effect. It indicates that there is a statistical significance between two possible outcomes and can be denoted as H1H1 or HaHa. For the above-mentioned example, the alternative hypothesis would be that girls are shorter than boys at the age of 5.

## Hypothesis Testing P Value

In hypothesis testing, the p-value is used to indicate whether the results obtained after conducting a test are statistically significant or not. It also indicates the probability of making an error in rejecting or not rejecting the null hypothesis. This value is always a number between 0 and 1.

## Hypothesis Testing Critical region

All sets of values that lead to rejecting the null hypothesis lie in the critical region. Furthermore, the value that separates the critical region from the non-critical region is known as the critical value.

These are the steps you’ll want to take to see if your suppositions stand up:

**State your null hypothesis**. The null hypothesis is a commonly accepted fact. It’s the default, or what we’d believe if the experiment was never conducted. It’s the least exciting result, showing no significant difference between two or more groups. Researchers work to nullify or disprove null hypotheses.**State an alternative hypothesis**. You’ll want to prove an alternative hypothesis. This is the opposite of the null hypothesis, demonstrating or supporting a statistically significant result. By rejecting the null hypothesis, you accept the alternative hypothesis.**Determine a significance level**. This is the determiner, also known as the alpha (α). It defines the probability that the null hypothesis will be rejected. A typical significance level is set at 0.05 (or 5%). You may also see 0.1 or 0.01, depending on the area of study. If you set the alpha at 0.05, then there is a 5% chance you’ll find support for the alternative hypothesis (thus rejecting the null hypothesis) when, in truth, the null hypothesis is true and you were wrong to reject it.**Calculate the p-value**. The p-value, or calculated probability, indicates the probability of achieving the results of the null hypothesis. While the alpha is the significance level you’re trying to achieve, the p-level is what your actual data is showing when you calculate it. A low p-value offers stronger support for your alternative hypothesis.**Draw a conclusion**. If your p-value meets your significance level requirements, then your alternative hypothesis may be valid and you may reject the null hypothesis. In other words, if your p-value is less than your significance level (e.g., if your calculated p-value is 0.02 and your significance level is 0.05), then you can reject the null hypothesis and accept your alternative hypothesis.

There are several types of hypothesis testing, and they are used based on the data provided. Depending on the sample size and the data given, we choose different hypothesis testing methodologies. Here starts the use of hypothesis testing tools in the research methodology.

**Normality-**This type of testing is used for normal distribution in a population sample. If the data points are grouped around the mean, the probability of them being above or below the mean is equally likely. Its shape resembles a bell curve that is equally distributed on either side of the mean.**T-test-**This test is used when the sample size in a normally distributed population is comparatively small, and the standard deviation is unknown. Usually, if the sample size drops below 30, we use a T-test to find the confidence intervals of the population.**Chi-Square Test-**The Chi-Square test is used to test the population variance against the known or assumed value of the population variance. It is also a better choice to test the goodness of fit of a distribution of data. The two most common Chi-Square tests are the Chi-Square test of independence and the chi-square test of variance.**ANOVA-**Analysis of Variance or ANOVA compares the data sets of two different populations or samples. It is similar in its use to the t-test or the Z-test, but it allows us to compare more than two sample means. ANOVA allows us to test the significance between an independent variable and a dependent variable, namely X and Y, respectively.**Z-test**– It is a statistical measure to test that the means of two population samples are different when their variance is known. For a Z-test, the population is assumed to be normally distributed. A z-test is better suited in the case of large sample sizes greater than 30. This is due to the central limit theorem that as the sample size increases, the samples are considered to be distributed normally.

Is it true that vitamin C has the ability to cure or prevent the common cold? Or is it just a myth? There’s nothing like an in-depth experiment to get to the bottom of it all. A potential hypothesis test could look something like this:

**Null hypothesis**— Children who take vitamin C are no less likely to become ill during flu season.**Alternative hypothesis**— Children who take vitamin C are less likely to become ill during flu season.**Significance level**— The significance level is 0.05.

4. **P-value** — The p-value is calculated to be 0.20.

5. **Conclusion** — After providing one group with vitamin C during flu season and the other with a placebo, you record whether or not participants got sick by the end of flu season. After conducting your statistical analysis of the results, you determine a p-value of 0.20. That is above the desired significance level of 0.05, and thus you fail to reject the null hypothesis. Based on your experiment, there is no support for the (alternative) hypothesis that vitamin C can prevent colds.

Examples of Hypothesis Testing: Real-World Scenarios (yourdictionary.com)

Hypothesis Testing | Introduction To Hypothesis Testing (analyticsvidhya.com)

Hypothesis Testing — Definition, Procedure, Types and FAQs (vedantu.com)

Hypothesis Testing — Definition, Examples, Formula, Types (cuemath.com)

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