In order to calculate the test statistics for one population mean, compare two means, a single population proportion, and two population proportions the test statistic calculator is used. It has the ability to summarize your data into a single number.

## What Is Test Statistics?

**“The measurement that evaluates the strength of evidence by refuting the hypothesis is known as test statistics”. **

It helps to determine the population hypothesis and helps us to summarize the data. Therefore it is also known as the significance hypothesis.

## Test Statistics Formula:

The test statistic formula calculator is used to evaluate the strength of evidence from the sample. However, the formula varies with the size of the population and the sample, and with these, you can evaluate how far your observed data is from the null hypothesis.

### One Population Mean:

For one population mean the test statistics formula is as follows:

$$ \frac{\overline{x} - μ_0}{\frac{σ}{\sqrt{n}}} $$

**Where:**

- Here, x̅ is the sample mean,
- μ0 is the population mean,
- σ is the standard deviation,
- n is the sample size.

### Comparing Two Means:

The formula to evaluate the independent samples are given below:

$$ \frac{\overline{x} - \overline{y}}{\sqrt{\frac{σ^2_x}{n_1} + \frac{σ^2_y}{n_2}}} $$

**Where:**

- x and y are the means
- σx are the standard deviation of the x values
- σy are the standard deviation of the y values
- n1 is the sample size of the x
- n2 is the sample size of the y

### Single Population Portion:

$$ \frac{\stackrel{\text{^}}{p} - \ p_0}{\sqrt{\frac{p_0(1-p_0)}{n}}} $$

**Where:**

- P is the sample proportion
- P0 is the claimed proportion
- n is the sample size

### Two Population Portions:

$$ \frac{\stackrel{\text{^}}{p_1} - \stackrel{\text{^}}{p_2}}{\sqrt{\stackrel{\text{^}}{p}(1-\stackrel{\text{^}}{p})(\frac{1}{n_1} + \frac{1}{n_2})}} $$

**Where:**

- P1 and P2 are the populations
- n1 and n2 are the sample sizes

## How to Calculate Test Statistic?

In this type of statistics, the quantitative measures assess the strength of evidence against the hypothesis. So look at the below example which indicates how the value of test statistic calculator summarizes your data into a single number.

### Example:

Suppose a cricket series was held against Pakistan and Sri Lanka in Colombo in which Baber Azam makes an average score of about 78 in five matches. As you know the average batting for a player is 40. In this case, the deviation in scoring is 4, what are the performance stats of Baber Azam?

**Given Data:**

- x = 78
- n = 5
- μ = 40
- Deviation = 4

#### Solution:

$$ \frac{\overline{x} - μ_0}{\frac{σ}{\sqrt{n}}} $$

$$ \text{Test Statistic}=\frac{78 – 40}{\frac{4}{\sqrt{5}}} $$

$$ \text{Test Statistic}=\frac{38}{\frac{4}{2.236}} $$

$$ \text{Test Statistic}=\frac{26}{1.79} $$

$$ \text{Test Statistic}= 14.53 $$

Suppose there is a 3% and it means that the performance for 5 matches is considerably better than average.

## Working of Sample Test Statistic Calculator:

The test value calculator transforms the data analysis by simplifying the hypothesis testing. Attach to the guide below to utilize the test statistics calculator.

**Input:**

- Choose the point that you want to calculate
- Put the values according to the chosen value
- Tap on
**“Calculate”**

**Output:**

Our standardized test statistic calculator will give you the following results.

- Test statistics for sample and population mean
- Complete calculation in the steps given

## Test Statistics Table:

### One Tail Table:

df | a = 0.1 | 0.05 | 0.025 | 0.01 | 0.005 | 0.001 | 0.0005 |
---|---|---|---|---|---|---|---|

∞ | t_{a} = 1.282 |
1.645 | 1.960 | 2.326 | 2.576 | 3.091 | 3.291 |

1 | 3.078 | 6.314 | 12.706 | 31.821 | 63.656 | 318.289 | 636.578 |

2 | 1.886 | 2.920 | 4.303 | 6.965 | 9.925 | 22.328 | 31.600 |

3 | 1.638 | 2.353 | 3.182 | 4.541 | 5.841 | 10.214 | 12.924 |

4 | 1.533 | 2.132 | 2.776 | 3.747 | 4.604 | 7.173 | 8.610 |

5 | 1.476 | 2.015 | 2.571 | 3.365 | 4.032 | 5.894 | 6.869 |

6 | 1.440 | 1.943 | 2.447 | 3.143 | 3.707 | 5.208 | 5.959 |

7 | 1.415 | 1.895 | 2.365 | 2.998 | 3.499 | 4.785 | 5.408 |

8 | 1.397 | 1.860 | 2.306 | 2.896 | 3.355 | 4.501 | 5.041 |

9 | 1.383 | 1.833 | 2.262 | 2.821 | 3.250 | 4.297 | 4.781 |

10 | 1.372 | 1.812 | 2.228 | 2.764 | 3.169 | 4.144 | 4.587 |

11 | 1.363 | 1.796 | 2.201 | 2.718 | 3.106 | 4.025 | 4.437 |

12 | 1.356 | 1.782 | 2.179 | 2.681 | 3.055 | 3.930 | 4.318 |

13 | 1.350 | 1.771 | 2.160 | 2.650 | 3.012 | 3.852 | 4.221 |

14 | 1.345 | 1.761 | 2.145 | 2.624 | 2.977 | 3.787 | 4.140 |

15 | 1.341 | 1.753 | 2.131 | 2.602 | 2.947 | 3.733 | 4.073 |

16 | 1.337 | 1.746 | 2.120 | 2.583 | 2.921 | 3.686 | 4.015 |

17 | 1.333 | 1.740 | 2.110 | 2.567 | 2.898 | 3.646 | 3.965 |

18 | 1.330 | 1.734 | 2.101 | 2.552 | 2.878 | 3.610 | 3.922 |

19 | 1.328 | 1.729 | 2.093 | 2.539 | 2.861 | 3.579 | 3.883 |

20 | 1.325 | 1.725 | 2.086 | 2.528 | 2.845 | 3.552 | 3.850 |

21 | 1.323 | 1.721 | 2.080 | 2.518 | 2.831 | 3.527 | 3.819 |

22 | 1.321 | 1.717 | 2.074 | 2.508 | 2.819 | 3.505 | 3.792 |

23 | 1.319 | 1.714 | 2.069 | 2.500 | 2.807 | 3.485 | 3.768 |

24 | 1.318 | 1.711 | 2.064 | 2.492 | 2.797 | 3.467 | 3.745 |

25 | 1.316 | 1.708 | 2.060 | 2.485 | 2.787 | 3.450 | 3.725 |

26 | 1.315 | 1.706 | 2.056 | 2.479 | 2.779 | 3.435 | 3.707 |

27 | 1.314 | 1.703 | 2.052 | 2.473 | 2.771 | 3.421 | 3.689 |

28 | 1.313 | 1.701 | 2.048 | 2.467 | 2.763 | 3.408 | 3.674 |

29 | 1.311 | 1.699 | 2.045 | 2.462 | 2.756 | 3.396 | 3.660 |

30 | 1.310 | 1.697 | 2.042 | 2.457 | 2.750 | 3.385 | 3.646 |

60 | 1.296 | 1.671 | 2.000 | 2.390 | 2.660 | 3.232 | 3.460 |

120 | 1.289 | 1.658 | 1.980 | 2.358 | 2.617 | 3.160 | 3.373 |

1000 | 1.282 | 1.646 | 1.962 | 2.330 | 2.581 | 3.098 | 3.300 |

### Two Tail Table:

df | a = 0.2 | 0.10 | 0.05 | 0.02 | 0.01 | 0.002 | 0.001 |
---|---|---|---|---|---|---|---|

∞ | t_{a} = 1.282 |
1.645 | 1.960 | 2.326 | 2.576 | 3.091 | 3.291 |

1 | 3.078 | 6.314 | 12.706 | 31.821 | 63.656 | 318.289 | 636.578 |

2 | 1.886 | 2.920 | 4.303 | 6.965 | 9.925 | 22.328 | 31.600 |

3 | 1.638 | 2.353 | 3.182 | 4.541 | 5.841 | 10.214 | 12.924 |

4 | 1.533 | 2.132 | 2.776 | 3.747 | 4.604 | 7.173 | 8.610 |

5 | 1.476 | 2.015 | 2.571 | 3.365 | 4.032 | 5.894 | 6.869 |

6 | 1.440 | 1.943 | 2.447 | 3.143 | 3.707 | 5.208 | 5.959 |

7 | 1.415 | 1.895 | 2.365 | 2.998 | 3.499 | 4.785 | 5.408 |

8 | 1.397 | 1.860 | 2.306 | 2.896 | 3.355 | 4.501 | 5.041 |

9 | 1.383 | 1.833 | 2.262 | 2.821 | 3.250 | 4.297 | 4.781 |

10 | 1.372 | 1.812 | 2.228 | 2.764 | 3.169 | 4.144 | 4.587 |

11 | 1.363 | 1.796 | 2.201 | 2.718 | 3.106 | 4.025 | 4.437 |

12 | 1.356 | 1.782 | 2.179 | 2.681 | 3.055 | 3.930 | 4.318 |

13 | 1.350 | 1.771 | 2.160 | 2.650 | 3.012 | 3.852 | 4.221 |

14 | 1.345 | 1.761 | 2.145 | 2.624 | 2.977 | 3.787 | 4.140 |

15 | 1.341 | 1.753 | 2.131 | 2.602 | 2.947 | 3.733 | 4.073 |

16 | 1.337 | 1.746 | 2.120 | 2.583 | 2.921 | 3.686 | 4.015 |

17 | 1.333 | 1.740 | 2.110 | 2.567 | 2.898 | 3.646 | 3.965 |

18 | 1.330 | 1.734 | 2.101 | 2.552 | 2.878 | 3.610 | 3.922 |

19 | 1.328 | 1.729 | 2.093 | 2.539 | 2.861 | 3.579 | 3.883 |

20 | 1.325 | 1.725 | 2.086 | 2.528 | 2.845 | 3.552 | 3.850 |

21 | 1.323 | 1.721 | 2.080 | 2.518 | 2.831 | 3.527 | 3.819 |

22 | 1.321 | 1.717 | 2.074 | 2.508 | 2.819 | 3.505 | 3.792 |

23 | 1.319 | 1.714 | 2.069 | 2.500 | 2.807 | 3.485 | 3.768 |

24 | 1.318 | 1.711 | 2.064 | 2.492 | 2.797 | 3.467 | 3.745 |

25 | 1.316 | 1.708 | 2.060 | 2.485 | 2.787 | 3.450 | 3.725 |

26 | 1.315 | 1.706 | 2.056 | 2.479 | 2.779 | 3.435 | 3.707 |

27 | 1.314 | 1.703 | 2.052 | 2.473 | 2.771 | 3.421 | 3.689 |

28 | 1.313 | 1.701 | 2.048 | 2.467 | 2.763 | 3.408 | 3.674 |

29 | 1.311 | 1.699 | 2.045 | 2.462 | 2.756 | 3.396 | 3.660 |

30 | 1.310 | 1.697 | 2.042 | 2.457 | 2.750 | 3.385 | 3.646 |

60 | 1.296 | 1.671 | 2.000 | 2.390 | 2.660 | 3.232 | 3.460 |

120 | 1.289 | 1.658 | 1.980 | 2.358 | 2.617 | 3.160 | 3.373 |

240 | 1.282 | 1.645 | 1.960 | 2.326 | 2.576 | 3.091 | 3.291 |

## FAQs:

### What indicates the negative test statistics?

A negative test statistics value indicates that it occurs on the left side of the mean. All left values are negative and all right values are positive. A negative test is just like a standard normal that has a zero mean.

### What are the applications of test statistics related to data sets?

- Product quality with the sample measurement
- Market research to analyze the survey data
- Strategies of investment and impact of market trends also analyzed
- Determination of significant change and physiological experiments

### What does a 0 test value mean?

If the test statistics value is equal to zero it means that sample results are equal to the null hypothesis.

## References:

From the source **Wikipedia: **Test statistic, Example.

From the source **Khan Academy:** Significance tests (hypothesis testing),