In this tutorial we are going to cover Hypothesis Testing. To understand this topic better, we would break it down into the following sub-topics

**1. About Estimation**

Estimation is a statistical way of trying to deduce the value of an unknown parameter. For example, to estimate the mean of a population µ, we can take a sample from the population and calculate the mean. Then we can use the sample mean as an estimate of the population mean.

**Point Estimate vs Interval Estimate**

*A point estimate* is one single number that is represents the parameter you are trying to estimate.

*Interval estimates* is a range of values that represents the parameter you are trying to estimate. Hence, interval estimate are often two values that define a range.

The question now is: how accurate is our estimate? We can get this by performing hypothesis testing.

**2. Introduction to Hypothesis Testing**

Hypothesis testing is simply a statistical way of testing an existing or null hypothesis H_{0}(that is an estimate the is currently accepted). Therefore, to carry out a hypothesis, there must at least be an existing hypothesis. So we have to test the null hypothesis to see if it is correct.

To do this we need to formulate an alternative hypothesis Ha or H_{1}. This is normally exactly opposite of the null hypothesis.

Let’s take example of regression from Machine Learning 101. We make an estimate of the regression coefficient β_{1} in case of linear regression.

Let’s state the null and alternate hypothesis:

- H
_{0}: β_{1}= 0 - H
_{a}: β_{1}≠ 0

To carry out hypothesis testing, we need to determine if our estimate for β_{1} is far enough from zero. In this case we would be confident that ≠ is non-zero.

**3. Confidence Interval**

How far is far enough depends on the standard error. The standard error is represented as SE(β_{1}) in case of β_{1}.

The standard error tells us how much our estimate differs from the actual value. In case of estimating the mean of a population

Where n is the sample size while σ is the standard deviation of the sample.

We also see that this formula show a relationship between the standard error and the sample size: the larger the sample size, the lower the standard error.

Standard errors can be used to compute confidence intervals. A 95% confidence interval means the range of values within which the the value of the unknown parameter can fall with a 95% probability. Therefore, confidence interval has an upper and lower limits.

For linear regression, a 95% confidence interval for β_{1} would mean:

β1 ± SE(β_{1})

That is 95% chance (or 0.95 probability) that the interval:

- upper: β
_{1}+ 2SE(β_{1}) - lower: β
_{1}+ 2SE(β_{1})

would contain the real value of β_{1}

**4. t-Statistic**

To actually carry out hypothesis testing, we compute the t-statistic. In the case of β_{1}, this is given by:

t = β_{1} / [SE(β_{1})]

This simply measures the number of standard deviations that β1 is away from 0. This in the case our linear regression example. The t-distribution which is assumed in this case, has a similar shape to normal distribution for n > 30.

**5. p-Value**

Recall that statistics relates with probability. So in case of t-statistic, we can compute the probability of observing any value that is equal to |t| or greater, assuming that β_{1} is 0.

This probability is what is know as the p-value.

a small value of p-value indicates that is is not likely to observe such a significant association between the X and Y (in case of linear regression) due to chance. Therefore, when a small p-value is determined, then we can conclude that there is a relationship between X and Y(the predictor and response variables). In this case, we reject the null hypothesis.

In inferential statistics, the null hypothesis is a general statement or default position that there is no relationship between two measured phenomena, or no association among groups.[1] Testing (accepting, approving, rejecting, or disproving) the null hypothesis —and thus concluding that there are or are not …

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