Data Analysis and Visualization t Test

Data analysis and Visualization t-test

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................2
Meaning of t-test..........................................................................................................................2
Hypothesis and how to develop null and alternative hypothesis.................................................2
Appropriate set of testable hypothesis.........................................................................................3
Testing value at different intervals..............................................................................................4
Presence of any outliers in the data.............................................................................................6
Any other features of the data that appear during your investigation of the dataset.................10
Any other factors that might affect the results...........................................................................10
CONCLUSION..............................................................................................................................10
REFERENCES..............................................................................................................................12

INTRODUCTION
Data analysis is a process that is systematically applied in order to evaluate the data and
derive better results as well. In the present report, the entire data shed a light upon data analysis
in which the dataset is chosen. The data is extracted from the authentic sites where it has been
identified that infection rate of Covid – 19 is varied between white people and BAME in the UK.
Also, the report will analyse which test is suitable to answer the question and along with this, test
will be conducted at different P level. Moreover, the outlier will be determined through the data
which might affect the results. Moreover, different factors might affect the results and this has
not been considered in a stated experiments.
Meaning of t-test
t-test is a type of an inferential statistic which is used to examine that there is a mean
difference between the two mean groups which is somehow related to the certain features. That
is why, it can be stated that with the help of this test, the statistician can determine the degree of
freedom which help to identify the statistical significance (Husain and Ardhiansyah, 2020).
However, it can be also stated that with this test, a scholar also allows to compare the average
value of tow dataset which assists to determine, only if they came from a same population. In the
present study, this is also used because we have two groups i.e. white people and BAME. That is
why, it is far beneficial to identify the different between a groups.
Hypothesis and how to develop null and alternative hypothesis
Hypothesis is an assumption which is proposed for a sake of argument that assists to
determine whether it is true or not. In a scientific methodology, it has been identified that
hypothesis is constructed before using any test so that effective results can be drawn.
In order to develop null hypothesis, start by asking a question like there is no relationship
or significant association between the variables. It is also represented as H0 whereas alternative
hypothesis is denoted as H1. On the other side, for alternative hypothesis, can write that there is a
relationship between two or more variable that assist to determine the observed pattern between
data and it is not due to a chance of occurrence (Foster and et.al., 2021). Through this statement,
both hypothesis can be formulated and by applying the test and review the significance value,
hypothesis can be proved by considering the standard criteria.

Appropriate set of testable hypothesis
H0 (Null hypothesis): There is no significant difference between the infection rate of Covid-19
with white people and BAME.
H1 (Alternative hypothesis): There is a significant difference between the infection rate of
Covid-19 with white people and BAME
Paired Samples Statistics
Mean N Std. Deviation Std. Error
Mean
Pair 1 White patient 36.5800 50 11.97973 1.69419
BAME 60.0400 50 23.39192 3.30812
Paired Samples Correlations
N Correlation Sig.
Pair 1 White patient &
BAME 50 -.045 .757
Paired Samples Test
Paired Differences t df Sig. (2-
tailed)Mean Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lower Upper
Pair
1
White
patient -
BAME
-
23.46000 26.75529 3.78377 -
31.06377
-
15.85623
-
6.200 49 .000
Interpretation: Through the above test, it has been identified that from sample paired
statistics that average number of White patient is 36.58 whereas BAME is 60. Also, with the help
of the significance value, it has been examined that there is a difference between infection rate of
pandemic with White and BAME people. It is so because the value of p (0.00) < 0.05 which in
turn reflect that alternative hypothesis is accepted over other. Milner and Jumbe (2020) also
reflected in their study that pandemic affected all type of people whether they are BAME, white