Chi Square Formula

5/5 - (1 vote)

Chi-square formula is used to compare two or more statistical data sets. The chi-square formula is used in data that consist of variables distributed across various categories and helps us to know whether that distribution is different from what one would expect by chance.

Example: You research two groups of women and put them in categories of student, employed or self-employed.

Group 1	Group 2
Student	40	30
Employed	89	67
Self-employed	3	7

The numbers collected are different, but you now want to know

Is that just a random occurrence? Or
Is there any correlation?

What is the Chi Square Formula?

The chi-squared test checks the difference between the observed value and the expected value. Chi-Square shows or in a way check the relationship between two categorical variables which can be can be calculated by using the given observed frequency and expected frequency.

The Chi-Square is denoted by χ². The chi-square formula is:

χ² = ∑(O_i – E_i)²/E_i

where

O_i = observed value (actual value)
E_i= expected value.

The Chi-Square test gives a P-value to help you know the correlation if any!

A hypothesis is in consideration, that a given condition or statement might be true, which we can test later. For example

A very small Chi-Square test statistic indicates that the collected data matches the expected data extremely well.
A very large Chi-Square test statistic indicates that the data does not match very well. If the chi-square value is large, the null hypothesis is rejected.

Chi-Square test statistic is called P-value. The P-value is short for probability value. It defines the probability of getting a result that is either the same or more extreme than the other actual observations. The P-value represents the probability of occurrence of the given event. The P-value is used as an alternative to the rejection point to provide the least significance for which the null hypothesis would be rejected. The smaller the P-value, the stronger is the evidence in favor of the alternative hypothesis given observed frequency and expected frequency.

P-value	Description	Hypothesis Interpretation
P-value ≤ 0.05	It indicates the null hypothesis is very unlikely.	Rejected
P-value > 0.05	It indicates the null hypothesis is very likely.	Accepted or it “fails to reject”.
P-value > 0.05	The P-value is near the cut-off. It is considered as marginal	The hypothesis needs more attention.

Applications of Chi Square Formula

Given below are a few most common applications of the chi-square formula

used by Biologists to determine if there is a significant association between the two variables, such as the association between two species in a community.
used by Genetic analysts to interpret the numbers in various phenotypic classes.
used in various statistical procedures to help to decide if to hold onto or reject the hypothesis.
used in the medical literature to compare the incidence of the same characteristics in two or more groups.

Chi-Square Distribution

When we consider, the null speculation is true, the sampling distribution of the test statistic is called as chi-squared distribution. The chi-squared test helps to determine whether there is a notable difference between the normal frequencies and the observed frequencies in one or more classes or categories. It gives the probability of independent variables.

Note: Chi-squared test is applicable only for categorical data, such as men and women falling under the categories of Gender, Age, Height, etc.

Finding P-Value

P stands for probability here. To calculate the p-value, the chi-square test is used in statistics. The different values of p indicates the different hypothesis interpretation, are given below:

P≤ 0.05; Hypothesis rejected
P>.05; Hypothesis Accepted

Probability is all about chance or risk or uncertainty. It is the possibility of the outcome of the sample or the occurrence of an event. But when we talk about statistics, it is more about how we handle various data using different techniques. It helps to represent complicated data or bulk data in a very easy and understandable way. It describes the collection, analysis, interpretation, presentation, and organization of data. The concept of both probability and statistics is related to the chi-squared test.

Properties

The following are the important properties of the chi-square test:

Two times the number of degrees of freedom is equal to the variance.
The number of degree of freedom is equal to the mean distribution
The chi-square distribution curve approaches the normal distribution when the degree of freedom increases.

Chi-Square Test of Independence

The chi-square test of independence also known as the chi-square test of association which is used to determine the association between the categorical variables. It is considered as a non-parametric test. It is mostly used to test statistical independence.

The chi-square test of independence is not appropriate when the categorical variables represent the pre-test and post-test observations. For this test, the data must meet the following requirements:

Two categorical variables
Relatively large sample size
Categories of variables (two or more)
Independence of observations

Examples Using Chi Square Formula

Example 1: Calculate the Chi-square value for the following data of incidences of water-borne diseases in three tropical regions.

	India	Equador	South America	Total
Typhoid	31	14	45	90
Cholera	2	5	53	60
Diarrhoea	53	45	2	100
	86	64	100	250

Solution:

Setting up the following table:

Observed	Expected	O_i – E_i	(O_i – E_i)²	(O_i – E_i)²/Ei
31	30.96	0.04	0.0016	0.0000516
14	23,04	9.04	81.72	3.546
45	36.00	9.00	81.00	2.25
2	20.64	18.64	347.45	16.83
5	15.36	10.36	107.33	6.99
53	24.00	29.00	841.00	35.04
53	34.40	18.60	345.96	10.06
45	25.60	19.40	376.36	14.70
2	40.00	38.00	1444.00	36.10

Answer: Chi Square = 125.516

Example 2: What conclusion should be made with respect to an experiment when the significance level is 0.05 (p = 0.05)?

Solution:

Since the p-value of 0.068 is greater than 0.05, it would fail to reject the null hypothesis.

Answer: As the value of p < 0.05, the null hypothesis is rejected.

Example 3: As per the survey on cars owned by each family in the locality the data has been arranged in the following table.

Number of cars	Oi	Ei
One car	30	25.6
Two cars	14	15
Three cars	6	5.2
Total	50

Solution:

Setting up the following table:

	Oi	Ei	(O_i – E_i)²	(O_i – E_i)²/E_i
One car	30	25.6	19.36	0.645
Two cars	14	15.1	1.21	0.086
Three cars	6	5.2	0.64	0.106
Total	50			0.837

Therefore, χ² = ∑(O_i – E_i)²/E_i = 0.837

Answer: Chi Square = 0.837

Example of Categorical Data

Let us take an example of a categorical data where there is a society of 1000 residents with four neighbourhoods, P, Q, R and S. A random sample of 650 residents of the society is taken whose occupations are doctors, engineers and teachers. The null hypothesis is that each person’s neighbourhood of residency is independent of the person’s professional division. The data are categorised as:

Categories	P	Q	R	S	Total
Doctors	90	60	104	95	349
Engineers	30	50	51	20	151
Teachers	30	40	45	35	150
Total	150	150	200	150	650

Assume the sample living in neighbourhood P, 150, to estimate what proportion of the whole 1,000 people live in neighbourhood P. In the same way, we take 349/650 to calculate what ratio of the 1,000 are doctors. By the supposition of independence under the hypothesis, we should “expect” the number of doctors in neighbourhood P is;

150 x 349/650 ≈ 80.54

So by the chi-square test formula for that particular cell in the table, we get;

(Observed – Expected)²/Expected Value = (90-80.54)²/80.54 ≈ 1.11

Some of the exciting facts about the Chi-square test are given below:

The Chi-square statistic can only be used on numbers. We cannot use them for data in terms of percentages, proportions, means or similar statistical contents. Suppose, if we have 20% of 400 people, we need to convert it to a number, i.e. 80, before running a test statistic.

A chi-square test will give us a p-value. The p-value will tell us whether our test results are significant or not.

However, to perform a chi-square test and get the p-value, we require two pieces of information:

(1) Degrees of freedom. That’s just the number of categories minus 1.

(2) The alpha level(α). You or the researcher chooses this. The usual alpha level is 0.05 (5%), but you could also have other levels like 0.01 or 0.10.

In elementary statistics, we usually get questions along with the degrees of freedom(DF) and the alpha level. Thus, we don’t usually have to figure out what they are. To get the degrees of freedom, count the categories and subtract 1.

FAQs on Chi Square Formula

What Is Chi Square Formula in Statistics?

Chi-square formula is a statistical formula to compare two or more statistical data sets. It is used for data that consist of variables distributed across various categories and is denoted by χ². The chi-square formula is: χ² = ∑(O_i – E_i)²/E_i, where O_i = observed value (actual value) and E_i= expected value.

How To Calculate p value from Chi Square Formula?

The Chi-Square test gives a P-value that helps determine the correlation.

A very small Chi-Square test statistic indicates that the collected data matches the expected data extremely well.
A very large Chi-Square test statistic indicates that the data does not match very well. If the chi-square value is large, the null hypothesis is rejected.

When To Use Chi Square Formula?

Chi square formula is used for statistical analysis but the given data should be frequencies rather than percentages or some other transformation of the data.

What Are the Applications of Chi Square Formula?

The applications of the chi-square formula are as follows

used by Biologists to determine if there is a significant association between the two variables, such as the association between two species in a community.
used by Genetic analysts to interpret the numbers in various phenotypic classes.
used in various statistical procedures to help to decide if to hold onto or reject the hypothesis.
used in the medical literature to compare the incidence of the same characteristics in two or more groups.

Table

The chi-square distribution table with three probability levels is provided here. The statistic here is used to examine whether distributions of certain variables vary from one another. The categorical variable will produce data in the categories and numerical variables will produce data in numerical form.

The distribution of χ²with (r-1)(c-1) degrees of freedom(DF), is represented in the table given below. Here, r represents the number of rows in the two-way table and c represents the number of columns.

DF	Value of P
DF	0.05	0.01	0.001
1	3.84	6.64	10.83
2	5.99	9.21	13.82
3	7.82	11.35	16.27
4	9.49	13.28	18.47
5	11.07	15.09	20.52
6	12.59	16.81	22.46
7	14.07	18.48	24.32
8	15.51	20.09	26.13
9	16.92	21.67	27.88
10	18.31	23.21	29.59
11	19.68	24.73	31.26
12	21.03	26.22	32.91
13	22.36	27.69	34.53
14	23.69	29.14	36.12
15	25.00	30.58	37.70
16	26.30	32.00	39.25
17	27.59	33.41	40.79
18	28.87	34.81	42.31
19	30.14	36.19	43.82
20	31.41	37.57	45.32
21	32.67	38.93	46.80
22	33.92	40.29	48.27
23	35.17	41.64	49.73
24	36.42	42.98	51.18
25	37.65	44.31	52.62
26	38.89	45.64	54.05
27	40.11	46.96	55.48
28	41.34	48.28	56.89
29	42.56	49.59	58.30
30	43.77	50.89	59.70
31	44.99	52.19	61.10
32	46.19	53.49	62.49
33	47.40	54.78	63.87
34	48.60	56.06	65.25
35	49.80	57.34	66.62
36	51.00	58.62	67.99
37	52.19	59.89	69.35
38	53.38	61.16	70.71
39	54.57	62.43	72.06
40	55.76	63.69	73.41
41	56.94	64.95	74.75
42	58.12	66.21	76.09
43	59.30	67.46	77.42
44	60.48	68.71	78.75
45	61.66	69.96	80.08
46	62.83	71.20	81.40
47	64.00	72.44	82.72
48	65.17	73.68	84.03
49	66.34	74.92	85.35
50	67.51	76.15	86.66
51	68.67	77.39	87.97
52	69.83	78.62	89.27
53	70.99	79.84	90.57
54	72.15	81.07	91.88
55	73.31	82.29	93.17
56	74.47	83.52	94.47
57	75.62	84.73	95.75
58	76.78	85.95	97.03
59	77.93	87.17	98.34
60	79.08	88.38	99.62
61	80.23	89.59	100.88
62	81.38	90.80	102.15
63	82.53	92.01	103.46
64	83.68	93.22	104.72
65	84.82	94.42	105.97
66	85.97	95.63	107.26
67	87.11	96.83	108.54
68	88.25	98.03	109.79
69	89.39	99.23	111.06
70	90.53	100.42	112.31
71	91.67	101.62	113.56
72	92.81	102.82	114.84
73	93.95	104.01	116.08
74	95.08	105.20	117.35
75	96.22	106.39	118.60
76	97.35	107.58	119.85
77	98.49	108.77	121.11
78	99.62	109.96	122.36
79	100.75	111.15	123.60
80	101.88	112.33	124.84
81	103.01	113.51	126.09
82	104.14	114.70	127.33
83	105.27	115.88	128.57
84	106.40	117.06	129.80
85	107.52	118.24	131.04
86	108.65	119.41	132.28
87	109.77	120.59	133.51
88	110.90	121.77	134.74
89	112.02	122.94	135.96
90	113.15	124.12	137.19
91	114.27	125.29	138.45
92	115.39	126.46	139.66
93	116.51	127.63	140.90
94	117.63	128.80	142.12
95	118.75	129.97	143.32
96	119.87	131.14	144.55
97	120.99	132.31	145.78
98	122.11	133.47	146.99
99	123.23	134.64	148.21
100	124.34	135.81	149.48

Solved Problem

Question:

A survey on cars had conducted in 2011 and determined that 60% of car owners have only one car, 28% have two cars, and 12% have three or more. Supposing that you have decided to conduct your own survey and have collected the data below, determine whether your data supports the results of the study.

Use a significance level of 0.05. Also, given that, out of 129 car owners, 73 had one car and 38 had two cars.

Solution:

Let us state the null and alternative hypotheses.

H₀: The proportion of car owners with one, two or three cars is 0.60, 0.28 and 0.12 respectively.

H₁: The proportion of car owners with one, two or three cars does not match the proposed model.

A Chi-Square goodness of fit test is appropriate because we are examining the distribution of a single categorical variable.

Let’s tabulate the given information and calculate the required values.

	Observed (O_i)	Expected (E_i)	O_i – E_i	(O_i – E_i)²	(O_i – E_i)²/E_i
One car	73	0.60 × 129 = 77.4	-4.4	19.36	0.2501
Two cars	38	0.28 × 129 = 36.1	1.9	3.61	0.1
Three or more cars	18	0.12 × 129 = 15.5	2.5	6.25	0.4032
Total	129				0.7533

Therefore, χ² = ∑(O_i – E_i)²/E_i = 0.7533

Let’s compare it to the chi-square value for the significance level 0.05.

The degrees for freedom = 3 – 1 = 2

Using the table, the critical value for a 0.05 significance level with df = 2 is 5.99.

That means that 95 times out of 100, a survey that agrees with a sample will have a χ² value of 5.99 or less.

The Chi-square statistic is only 0.7533, so we will accept the null hypothesis.

Frequently Asked Questions – FAQs

What is the chi-square test write its formula?

When we consider the null hypothesis is true, the test statistic’s sampling distribution is called chi-squared distribution. The formula for chi-square is:
χ^2 = ∑(O_i – E_i)^2/E_i
Here,
O_i = Observed value
E_i = Expected value

How do you calculate chi squared?

The value of the Chi-squared statistic can be calculated using the formula given below:
χ^2 = ∑(O_i – E_i)^2/E_i
This can be done as follows.
For each observed number in the data, subtract the corresponding expected value, i.e. (O — E).
Square the difference, (O — E)^2
Divide these squares by the expected value of each observation, i.e. [(O – E)^2 / E].
Finally, take the sum of these values.
Thus, the obtained value will be the chi-squared statistic.

What is a chi-square test used for?

The chi-squared test is done to check if there is any difference between the observed value and the expected value.

How do you interpret a chi-square test?

For a Chi-square test, a p-value that is less than or equal to the specified significance level indicates sufficient evidence to conclude that the observed distribution is not the same as the expected distribution. Here, we can conclude that a relationship exists between the given categorical variables.