The independent variable in the Stroop Effect experiment is whether the color word matches or mismatches the color of the ink. The dependent variable is reaction time. It is hypothesized that reaction time will be lower for color-word matches than mismatches. Descriptive statistics on the dataset show higher mean and variability for the incongruent condition. A t-test was conducted and found a statistically significant difference between conditions, rejecting the null hypothesis. This matches expectations that it takes longer to name a color when it mismatches the written word.

1. Stroop Effect P1
Q: What is our independent variable? What is our dependent variable?
A: The independent variable is the two forms of the test either color matching word or color
mismatching word. The dependent variable is reaction time between the task and response.[1]
Q: What is an appropriate set of hypotheses for this task? What kind of statistical test do you
expect to perform? Justify your choices.
A: Two hypotheses are most likely to occur. Either there will be no too little effect in the test
results creating a null hypothesis. However, an alternate hypothesis may show results that color
matching word has a lower response time than color mismatching word.[1]
Indicating that the
incongruent’s reaction time data is significantly higher than the congruent’s reaction time data.
• Null Hypothesis H0 = The data between the two results will not differ significantly. In
which the t-statistic do not differ from alpha of 0.05. In which Ho : µ1 = µ2
[7]
• Alternative Hypothesis H1 = There will be a non-random significant difference between
the data of the two results. In which the t-statistic differ from alpha of 0.05. In which H1 :
µ1 = µ2
[7]
• µ1 is the mean of population 1 and µ2 is the mean of population 2.
We can use a two sample T-Test considering the sample size is less than 30 and the standard
deviation of the population is unknown.[6]
Also both populations are independent for data, but
each person has two measurements.[5]
Q: Report some descriptive statistics regarding this dataset. Include at least one measure of
central tendency and at least one measure of variability.
A:
Congruent Incongruent
Mean 14.05 22.02
Mode N/A N/A
Median 14.36 21.02
Variance 12.67 23.01
Std.Deviation 3.56 4.80
IQR 4.31 5.33
Range 13.70 8.84
Q: Provide one or two visualizations that show the distribution of the sample data. Write one or
two sentences noting what you observe about the plot or plots.
Fig 1: Statistical analysis of
Congruent and Incongruent
Stroop data.

2. .
We can see from all three figures that on average the reaction time when performing
congruent test is lower than that of the incongruent test for the subjects. From figure 2 with
removal of the outlier using the IQR range we can see the congruent curve is positively skewed,
while the incongruent curve is more of a normal distribution. This might be indicative that some
0
2
4
6
8
0 4 8 12 16 20 24 28 32 36 40
Frequency
Reaction Time (seconds)
Congruent Vs. Incongruent
Without Ourliers
Congruent
Incongruent
0
2
4
6
8
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
Frequency
Reaction Time (s)
Congruent Vs. Incongruent
Without Ourliers
Congruent
Incongruent
0.00
5.00
10.00
15.00
20.00
25.00
Congruent Incongruent
ReactionTime(S)
Average Reaction Time
Congruent Vs. Incongruent
Fig 2: Congruent Vs
Incongruent histogram with
outliers.[2]
Fig 3: Congruent Vs
Incongruent histogram
without outliers.[2]
Fig 4: Average reaction time
of Congruent Vs Incongruent

3. individuals are not as greatly affected by the incongruent task as others. While most individuals
are able to perform the congruent task with ease.
Q: Now, perform the statistical test and report your results. What is your confidence level and
your critical statistic value? Do you reject the null hypothesis or fail to reject it? Come to a
conclusion in terms of the experiment task. Did the results match up with your expectations?
A: Confidence level is 95%, Critical statistic value 0.05[3]
Congruent Incongruent Difference
Mean 14.05 22.02 -7.96
Variance 12.67 23.01
Observations 24 24
Pearson Correlation 0.35
Hypothesized Mean Difference 0
df 23
t Stat -8.0207
P(T<=t) one-tail 2.052E-08
t Critical one-tail 1.7139
P(T<=t) two-tail 4.103E-08
t Critical two-tail 2.069
Since P-Value two-tail for the T-Test is less than critical statistic value of 0.05. In conclusion I
reject the null hypothesis and support the alternative hypothesis.[3]
. There is a significant
difference between mean times for the task. It does take significantly longer to complete the
incongruent task as compared to the congruent task. This is seen by the difference of the
means of approximately -7.96 seconds. These results do match my expectations and are in line
with the results I received when taking the Stroop test myself.
Q: What do you think is responsible for the effects observed? Can you think of an alternative or
similar task that would result in a similar effect? Some research about the problem will be
helpful for thinking about these two questions!
A: This may be due to humans being conditioned to deciphering words in everyday life that our
brains automatically focus on figuring out the word and ignore color initially unless forced to.
This reduces the reaction time of the person as they have use time confirming the color. You
could perform a numerical test where the numbers differ in both numerical and physical size and
ask participants which number is numerically larger.[4]
References:
[1]: Stroop, J.R. Studies of interference in serial verbal reactions. J. Exp. Psychol., 18:643-662,
1935
Fig 4: Two Sample T-Test[3]

4. [2]: Create A Histogram. (n.d.). Retrieved January 17, 2017, from
https://support.office.com/en-us/article/Create-a-histogram-b6814e9e-5860-4113-ba51-
e3a1b9ee1bbe
[3]: Chieh, C. J. (n.d.). Making Sense of the Two-Sample T-Test. Retrieved January 17,
2017, from https://www.isixsigma.com/tools-templates/hypothesis-testing/making-sense-two-sample-
t-test/
[4]: Henik, A. & Tzelgov, J. Mem Cogn (1982) 10: 389. doi:10.3758/BF03202431
[5]: Types of T-Test. Retrieved January 17, 2017. from http://support.minitab.com/en-
us/minitab/17/topic-library/basic-statistics-and-graphs/hypothesis-tests/tests-of-means/types-of-
t-tests/
[6]: Andale. T-Score Vs. Z-Score What’s the difference? Published August 23, 2013,
Retrieved January 17, 2017. From http://www.statisticshowto.com/when-to-use-a-t-score-vs-z-
score/
[7]: What is Hypothesis Testing?. Retrieved January 17, 2017. From
http://stattrek.com/hypothesis-test/hypothesis-testing.aspx