Hypothesis testing is just a method for testing a claim or hypothesis about a parameter in a population [mean, proportion]. In hypothesis testing we study the sample. Results of the sample are generalized to entire population.

The

denoted as H0 , this means testing a claim that already has some established parameters.*“Null Hypothesis”*The

is denoted as H1, this is known as the research hypothesis. It involves the claim to be tested.*“Alternative Hypothesis”*Four steps of hypothesis testing are:

(I) We state the Hypothesis

(II) Set the criteria for a decision

(III) Compute the test statistic

(IV) Make a decision

Hypothesis testing is just a method for testing a claim or hypothesis about a parameter in a population, using data measured in a sample. The goal of hypothesis testing is to determine the likelihood that a population parameter, such as the mean (µ), is likely to be true.

The ** “Null Hypothesis”** denoted as H0, this means testing a claim that already has some established parameters. The null hypothesis is always the accepted fact. It is a starting point. We test whether the value stated in the null hypothesis is likely to be true.

The ** “Alternative Hypothesis”** is denoted as H1, this is known as the research hypothesis. It involves the claim to be tested. An alternative hypothesis (H0) is a statement that directly contradicts a null hypothesis by stating that that the actual value of a population parameter

**is less than, greater than, or not equal**to the value stated in the null hypothesis. The alternate hypothesis is formulated depending on whether a one-tail or two-tail test is required:

**Purpose of a t-test.**

With the normal distribution we use z-scores and we must know the population’s standard deviation (σ) to calculate Z. But in the real world we often do not know the population standard deviation. Using the t-table, the t-test determines if there is a significant difference between two sets of data.

Due to variance and outliers, it is not enough just to compare mean values. A t-test also considers sample variances.

**TYPES OF T-TEST**

**One sample t-test**

Tests the null hypothesis that the population mean is equal to a specified value µ based on a sample mean.

Formula for t-statistic

**Independent two-sample t-test**

Test the null hypothesis that two sample means x1 and x2 are equal.

Example: We need to check if the mean test scores of 2 separate samples of students have a statistically significant difference.

The calculation of the t-statistic differs slightly for the following scenarios:

-equal sample size, equal variance

-unequal sample size, equal variance

-equal or unequal sample size, unequal variance

**Dependent, paired-sample t-test**

We use this type of t-test when the samples are dependent:

- One sample has been tested twice (repeated measurements)

- Two samples have been matched or “paired”

Example: We need to check of the same group of students has improved results on test scores before preparation course and after preparation course.

**Example #1**

We have daily production data over the same 10 days. Company wants to know if there is a significant difference in production between these to car plants.

From this sample, it looks like Car Plat 1 produce 36 more cars per day than Car Plant 2. Is this statistically significant more amount of cars?

Ho: X1 <= X2, Null hypothesis

Ha: X1>X2, Alternative hypothesis

one tailed test

You can compute the variance using VAR.S() function or Data Analysis > Descriptive Statistics. Our two samples have similar variances. I’m going to use t-Test: Two-Sample Assuming Equal Variances.

Compare t-value (2.28) to the critical value (1.734): 2.28 > 1.73

Since our computed t-value is greater than the critical value, we reject the null hypothesis. We believe with 95% confidence that Car Plant 1 produces more cars per day than Car Plant B.

IF P-VALUE IS LOW P<=α, THE HULL HYPOTHESIS MUST GO [REJECT]

**Example #2: ****t-Test: Two-Sample**

Do full time students spend more time studying stats than part time students. Test at α=0.05.

µf = population mean study time for full time students

µp = population mean study time for part time students

Ha: µf > µp **Ha: µf - µp > 0 Alternative hypothesis**

**Ho: µf - µp <=0 Null hypothesis**

**Full time: **3.2 1.5 6.5 0.2 3.7 3.3 1.7 3.6 3.8 5.3 6.9 3.6 1.7 5.2 5.2 1.2 7.2 3.9 1.9 5.3

**Part time: **3.1 3.4 4.6 2.8 2.3 1.5 3.8 9.5 4.3 2.7 1.6 1.6 3.2 4.2 3.9 1.2

**Ha: µf - µp > 0, **The alternative hypothesis says we must conduct right-tailed test

Conduct t-test.

1.

2.

The calculated t-value is OUTSIDE THE REJECTION REGION. And p-value > α. There is not enough evidence to conclude that full time students spend more time studying stats than part time students.

**P(T<=t) one-tail > α **

** 0.28 > 0.05**

We fail to reject the null hypothesis: Ho: µf - µp <=0

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