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Observed Values and Critical Values

When conducting statistical analyses, many numbers, charts and graphs are outputted. These can be confusing, and it can be challenging to determine what exactly they mean and how we can identify if these figures support or disprove our hypothesis. The two crucial statistical values to determine this are observed values and critical values.

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Observed Values and Critical Values

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When conducting statistical analyses, many numbers, charts and graphs are outputted. These can be confusing, and it can be challenging to determine what exactly they mean and how we can identify if these figures support or disprove our hypothesis. The two crucial statistical values to determine this are observed values and critical values.

  • First, we will delve into the observed value meaning and then move on to understand critical values psychology.
  • Then we will learn how hypothesis testing critical values work.
  • To help with your learning, we will look at a critical and observed value psychology example and provide you with a critical values table for the Mann-Whitney U test.

Observed Value Meaning

In statistical analyses, the observed value is one of the most important figures as it allows researchers to identify if their data follows the trend predicted in their hypothesis.

A hypothesis is a predictive statement that states what the researcher expects to find in their study.

The observed value is found when carrying out statistical tests called inferential tests.

Some examples of inferential tests are the chi-squared test, Mann-Whitney U test, Wilcoxon, and Spearman's Rho test.

Now that we've figured out the use of the observed value let's explore the observed value meaning.

The observed value meaning is the statistical figure that calculates/ measures the trend/ pattern investigated in the research.

The observed value differs depending on what inferential test is used. For instance, a correlation measures the association/ relationship between variables; using the figure r. In contrast, a t-test compares the mean of two groups; using the figure t.

Critical Values Psychology

But how do we know if the observed value we found is significant? And this is where the critical value comes in.

The critical value is a set value that we look at to see if what we have found (the observed value) is due to the variables investigated or chance.

The process involves comparing the observed value to the critical value provided by the statistical test we decide to use (this is why it's essential to ensure you're using the proper test).

First, we need a significance level (p-value) to do this. Usually, the significance level is p = 0.05, although this value can change.

The 'p' stands for probability.

A p-score of .05 suggests there is a 5% probability the results we found are due to chance. And p = .01 means there is a 1% probability of the results being due to chance.

The 0 before the decimal point in critical values is not reported per APA standards.

Hypothesis Testing Critical Values

There are different rules regarding the critical value in the tests we mentioned above, the chi-squared test, Mann Whitney U test, Wilcoxon, and Spearman's Rho test.

  • Chi-squared test: significant if the observed value (χ2) is equal to or larger than the critical value

  • Mann-Whitney U test: significant if the observed value (U) is equal to or smaller than the critical value

  • Wilcoxon test: significant if the observed value (T) is equal to or smaller than the critical value

  • Spearman's Rho test: significant if the observed value (r) is equal to or larger than the critical value

When using a critical values table, the observed value can be compared to the critical value to see if the results are statistically significant. Each statistical test will have its own critical values table. The critical value we need also depends on if our hypothesis is one or two-tailed.

A one-tailed hypothesis is when the researcher predicts that the findings will go in a particular direction, e.g. getting more sleep will lead to better exam grades.

A two-tailed hypothesis is a predictive statement that suggests there will be an effect, but the direction is unsure, e.g. if there will be an increase or decrease.

We need to know two essential things for the critical values table: the 'N' number (number of participants) and the 'df' (degrees of freedom). Each critical table will have a column of N values or df values depending on what test it is.

  • N = number of participants. In an independent group design, there will be different N numbers for each group of participants; this is written as Na (group A) and Nb (group B).
  • df = degrees of freedom refers to the elements allowed to vary in statistical tests. It is used for tests where the number of categories is important, such as the chi-square test (which compares nominal data for different categories to see if there are any differences). The more degrees of freedom, the more categories there are.

We need to look at the N or df column in critical tables provided by statistical tests to find a comparable critical value. Then we can compare the observed value to the critical value and decide on significance based on the test parameters we covered above.

Let's look at an example to help you make sense of this.

Critical and Observed Value Psychology Example

The Mann-Whitney U test compares the scores between two groups (independent groups design), focusing on ranks and ordinal data. Let's look at the steps involved to see if our results are significant.

As we see in this table, ten participants are in each group.

Group A scoresGroup B scores
324
56
84
1222
210
918
1120
151
147
1719

We need to work out the observed value, 'U'. To do this, we need to calculate scores for the two groups (Ua and Ub). The U score will be the lower score of the two.

First, we need to rank each score; this is done for both groups. The highest score is rank 1, the one after that is rank 2, and so on.

Group A scoresRankGroup B scoresRank
318241
516615
813417
129222
2191011
912185
1110203
157120
148714
176194

Now let's work out at Ua. We need to know Na and Nb, the total number of scores in each group. There were 10 participants in each group, so a total of 10 scores for each group, Na = 10 and Nb = 10.

First, multiply Na and Nb (10 x 10 = 100)

Then multiply Na by (Na + 1) and then divide by 2 (10 x 11/2 = 110/2 = 55) and add the two scores together (100 + 55 = 155). Afterwards, Group A's ranks should be added together (18 + 16 + 13 + 9 + 19 + 12 + 10 + 7 + 8 +6 = 118). And finally, subtract this from the number in the last step (155 - 118 = 37).

Thus, Ua = 37

Then, repeat for Ub; we won't go over the steps again. In this case Ub = 63.

The U value is the lower of the two, so here, U = 37.

Next, we need to note if our hypothesis is one or two-tailed and the p-value. Let's suppose our hypothesis is one-tailed with a p-value of 0.05.

Then we must consult our critical values table for the Mann-Whitney U test.

How we do calculations depends on the test used because the various inferential tests measure/ calculate different things.

Critical Values Table

The critical values table for the Mann-Whitney U test, p ≤ 0.05 (one-tailed), p ≤ 0.10 (two-tailed).

Nb

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Na

5

4

5

6

8

9

11

12

13

15

16

18

19

20

22

23

25

6

5

7

8

10

12

14

16

17

19

21

23

25

26

28

30

32

7

6

8

11

13

15

17

19

21

24

26

28

30

33

35

37

39

8

8

10

13

15

18

20

23

26

28

31

33

36

39

41

44

47

9

9

12

15

18

21

24

27

30

33

36

39

42

45

48

51

54

10

11

14

17

20

24

27

31

34

37

41

44

48

51

55

58

62

11

12

16

19

23

27

31

34

38

42

46

50

54

57

61

65

69

12

13

17

21

26

30

34

38

42

47

51

55

60

64

68

72

77

13

15

19

24

28

33

37

42

47

51

56

61

65

70

75

82

84

14

16

21

26

31

36

41

46

51

56

61

66

71

77

82

87

92

15

18

23

28

33

39

44

50

55

61

66

72

77

83

88

94

100

16

19

25

30

36

42

48

54

60

65

71

77

83

89

95

101

107

17

20

26

33

39

45

51

57

64

70

77

83

89

96

102

109

115

18

22

28

35

41

48

55

61

68

75

82

88

95

102

109

116

123

19

23

30

37

44

51

58

65

72

80

87

94

101

109

116

123

130

20

25

32

39

47

54

62

69

77

84

92

100

107

115

123

130

138

The values we need have been highlighted. First, we find Na, which in our case is 10. Then we find Nb, which is 10 too. We find the value where these two meet, which is the critical value. Here it is, 27.

Our observed value is 37, which is larger than the critical value of 27.

Therefore, our results are not significant, so we can accept the null hypothesis and reject the alternative hypothesis.

From this critical and observed psychology example, we can infer there is no statistical difference between the scores of the two groups. i.e. the null hypothesis is accepted.


Observed Values and Critical Values - Key takeaways

  • The observed value meaning is the statistical figure that calculates/ measures the trend/ pattern investigated in the research.
  • Critical values psychology is a statistical figure used to identify if the results from inferential tests are significant or if they occurred due to chance.
  • The critical and observed value psychology research is compared to identify whether the null or alternative hypothesis should be accepted/ rejected.
  • The critical values table allows the comparison of observed and critical values to identify if the results are statistically significant.
  • How researchers calculate and make inferences based on the observed and critical values depends on the inferential test the researcher uses.

Frequently Asked Questions about Observed Values and Critical Values

Critical values psychology is a statistical figure used to identify if the results from inferential tests are significant or if they occurred due to chance.

The observed value is the result obtained from a statistical test. We can compare the observed value to the critical value to see if it is significant or not.

Finding the observed value depends on the statistical test used and can be found using calculations and the corresponding critical values table. 

Hypothesis testing critical values is when we look to see if what we have found is due to the variables proposed in the hypothesis or chance. For some tests, the observed value needs to be the same or lower than the critical value to be significant. It needs to be the same or higher than the critical value for other tests to be significant. 

The observed value meaning is the statistical figure that calculates/ measures the trend/ pattern investigated in the research. 

Critical values psychology is a statistical figure used to identify if the results from inferential tests are significant or if they occurred due to chance.

Test your knowledge with multiple choice flashcards

What is a one-tailed hypothesis?

The observed value always needs to be higher than the critical value to be significant. True or false?

Chi-squared test:             if the observed value (χ2)  is equal to or larger than the critical value

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