Nama : DEFITTA ARDIMAN PUTRI

Session :17/C

NPM : 17040075

Hypothesis

A. Concept Hypothesis

A hypothesis (plural hypotheses) is a proposed explanation for a phenomenon. For a

hypothesis to be a scientific hypothesis, the scientific method requires that one
can test it. Scientists generally base scientific hypotheses on previous observations that
cannot satisfactorily be explained with the available scientific theories. Even though the
words "hypothesis" and "theory" are often used synonymously, a scientific hypothesis is not
the same as a scientific theory. A working hypothesis is a provisionally accepted hypothesis
proposed for further research, in a process beginning with an educated guess or thought. A
different meaning of the term hypothesis is used in formal logic, to denote the antecedent of
a proposition; thus in the proposition "If P, then Q", P denotes the hypothesis (or
antecedent); Q can be called a consequent. P is the assumption in
(possibly counterfactual) What If question. The adjective hypothetical, meaning "having the
nature of a hypothesis", or "being assumed to exist as an immediate consequence of a
hypothesis", can refer to any of these meanings of the term "hypothesis".
(https://en.wikiquote.org/wiki/Hypothesis)

A hypothesis is a testable explanation of cause and effect. A scientific hypothesis must be

a testable hypothesis. Hypotheses that cannot be tested, such as cause and effect attributed to
a supernatural being or an invisible fifth dimension that cannot be detected, are not part of
science. They are pseudo science. A hypothesis is a statement about the relationship between
two variables (usually, the IV and the DV). The statement must usually also be
operationalised or 'testable', which is another way of saying made more specific so that the
researcher knows exactly how to go about studying the relationship between the two
variables based on.

A good hypothesis is a productive one. A productive hypothesis can:

  Be easily learned and applied

 Explain the past accurately and persuasively

 Make accurate predictions about the future

 Generate new even more useful hypotheses

 Be applied to a wide variety of situations

 Be easily tested

The more of these attributes a hypothesis has, the more problems it can solve. The six most
common forms of hypotheses are:

A simple hypothesis is a prediction of the relationship between two variables:

the independent variable and the dependent variable.

 Drinking sugary drinks daily leads to obesity.

A complex hypothesis examines the relationship between two or more independent
variables and two or more dependent variables.
 Overweight adults who 1) value longevity and 2) seek happiness are more likely than
other adults to 1) lose their excess weight and 2) feel a more regular sense of joy.
A null hypothesis (H0) exists when a researcher believes there is no relationship
between the two variables, or there is a lack of information to state a scientific
hypothesis. This is something to attempt to disprove or discredit.
 There is no significant change in my health during the times when I drink green tea
only or root beer only. This is where the alternative hypothesis (H1) enters the scene.
In an attempt to disprove a null hypothesis, researchers will seek to discover an
alternative hypothesis.
 My health improves during the times when I drink green tea only, as opposed to root
beer only.
A logical hypothesis is a proposed explanation possessing limited evidence.
Generally, you want to turn a logical hypothesis into an empirical hypothesis, putting
your theories or postulations to the test.
 Cacti experience more successful growth rates than tulips on Mars. (Until we're able
to test plant growth in Mars' ground for an extended period of time, the evidence for
 this claim will be limited and the hypothesis will only remain logical.) An empirical
hypothesis, or working hypothesis, comes to life when a theory is being put to the test,
using observation and experiment. It's no longer just an idea or notion. It's actually
going through some trial and error, and perhaps changing around those independent
variables.
 Roses watered with liquid Vitamin B grow faster than roses watered with liquid
Vitamin E. (Here, trial and error is leading to a series of findings.)
A statistical hypothesis is an examination of a portion of a population.
 If you wanted to conduct a study on the life expectancy of Savannians, you would
want to examine every single resident of Savannah. This is not practical. Therefore,
you would conduct your research using a statistical hypothesis, or a sample of the
Savannian population.
(https://examples.yourdictionary.com/examples-of-hypothesis.html)

B. Directional And Non Directional Hypothesis

1. Directional Hypothesis
A directional/non-directional hypothesis is a more specified version of the alternate
hypothesis. A directional hypothesis notes the direction in which the predicted difference or
relationship between the variables will go e.g. Group A will be significantly better/worse than
Group B in Activity A. A directional (or one tailed hypothesis) states which way you think
the results are going to go, for example in an experimental study we might say…”Participants
who have been deprived of sleep for 24 hours will have more cold symptoms in the following
week after exposure to a virus than participants who have not been sleep deprived”; the
hypothesis compares the two groups/conditions and states which one will ….have more/less,
be quicker/slower, etc. If we had a correlational study, the directional hypothesis would state
whether we expect a positive or a negative correlation, we are stating how the two variables
will be related to each other, e.g. there will be a positive correlation between the number of
stressful life events experienced in the last year and the number of coughs and colds suffered,
whereby the more life events you have suffered the more coughs and cold you will have
had”. The directional hypothesis can also state a negative correlation, e.g. the higher the
number of face-book friends, the lower the life satisfaction score ”
 2. Non-directional hypothesis
A non directional hypothesis simply predicts there will be a difference between the
variables without specifying its direction e.g. There will be a difference between Group A
and Group B in Activity A. A non-directional (or two tailed hypothesis) simply states that
there will be a difference between the two groups/conditions but does not say which will be
greater/smaller, quicker/slower etc. Using our example above we would say “There will be a
difference between the number of cold symptoms experienced in the following week after
exposure to a virus for those participants who have been sleep deprived for 24 hours
compared with those who have not been sleep deprived for 24 hours.”
When the study is correlational, we simply state that variables will be correlated but do not
state whether the relationship will be positive or negative, e.g. there will be a significant
correlation between variable A and variable B.

https://www.mytutor.co.uk/answers/23601/GCSE/Psychology/What-is-the-difference-
between-the-null-hypothesis-alternate-hypothesis-directional-hypothesis-and-non-directional-
hypothesis/

C. The Principle Of Formulating A Hypothesis

The principal investigator then formulated a series of hypotheses as explicitly as possible.

In general they concerned, first, the individual's psychological readiness to participate in the
screening program (itself based on 3 separate psychological factors); second, the role of
situational factors in facilitating or inhibiting the readiness; and, third, the role of certain cues
or stimuli to action. [T] he attempt was made to define the hypotheses operationally, which
means defining them in terms that are measurabl.In the attempt to formulate hypotheses
operationally. it was necessary to define certain terms that might not at first seem to need
much further definition. Consider, for example, the phrase: the act of obtaining a chest X-ray

Is the person who obtains X-ray only after the appearance of suspicious symptoms taking
the same action as the person who obtains it in the absence of symptoms? Is the person who
obtains the X-ray at a mobile unit behaving in a way similar to the person who turns to his
physician or to the hospital? The action itself appears identical on the surface, but the
motivation, the context, the very nature of the behavior can be regarded as very different.
This consideration led to defining the phrase, “obtaining a chest x-ray,” in terms of 4 factors:
(1) whether the X-rayed person had voluntarily obtained a chest X-ray without compulsion or
pressure, (2) under what specific circumstances he had sought X-ray, (3) to what kind of X-
ray facility he had turned, and (4) the time period in which he had obtained X-ray.

Here are 7 steps to take to formulate a strong A/B testing hypothesis

a) Define your problem
Defining your problem is the first thing that needs to be done. What is it that
you want to test or solve? Is it to double your sales or to increase the number of
opt-ins? Whatever your goals are, they need to be clearly defined, quantifiable,
and measurable. This should give you a clear idea of what your new design
should solve including the process that will be followed to achieve the results
b) Find out the reasons behind the numbers
Now that you have defined your problem and you have a clear picture of what it
is you want to achieve, the next thing that follows is an in-depth analysis of the
current problem. This can be equated to sharpening the ax. Basically, you want
to take as much time as possible to learn the reasons behind your numbers. You
won’t be able to form an accurate hypothesis without studying what is happening in
the website where you want to test your A/B test. Now that you are already looking
for better variables to improve your conversion rates, it is only logical that you find
the reasons that brought you to this current situation. Why are you experiencing high
bounce rate? Why aren’t you seeing more conversions? Why are most of your
customers failing to complete the payment process? These are obviously some of the
reasons that may push you to improve your website. The only way to discover areas
of improvement in your website is to study your target market. You also need to get
customer feedback through comments, social media, surveys and email.
c) Talk to your visitors
It is important to get real feedback from your visitors. One way is to use
surveys—both entry surveys and exit surveys that are used to discover your
visitor’s objectives and determine whether their goals have been met respectively
this is aimed at understanding what they want or what their desires are.
Knowing the reasons behind their decisions and actions is the most important part of
the survey. Therefore, do not hesitate to ask them to give reasons for their actions in
the survey. For instance, you can place an exit survey at the end of a buying process
to ask them why they bought your product. You could also place an exit survey
immediately they abandoning a buying process to understand why they did so. You
could also use analytics tools to gather quantitative data such as location, devices
used, bounce rates and number of visitors and so on. In other words, both surveys and
analytics tools can complement each other when it comes to gathering information
about the customer.
d) Use segmentation to get actionable data
In statistics, averages don’t tell you the whole story. Segments do, and that is
why segmentation is an important step in the formulation of hypothesis. For
example, an experiment may show that a certain product is not performing well,
but upon further analysis, it may be discovered that majority of people who buy
the product are women aged between 18 and 29 years. Upon further
investigation, it may turn out that ads for the product were being targeted to the
general population. So when you do segmentation, it may eventually occur to
you that you should concentrate your marketing efforts on the women who fall in
the 18-29 age bracket. In other words, segmentation gives you actionable data,
which would otherwise be useless without it. There are many approaches to
segmentation. Examples include:

e) Source segmentation
involve separating the visitors who come to your site based on their sources e.g
desktop or mobile, android or iOS, email or social media. Find all the metrics
associated with all these segments such as bounce rates, number of visitors,
conversion rates and regions.

f) Behavioral segmentation
See what elements your customers focus most in your website. These days there are
eye-tracking technologies that make it possible to study the elements that attract the
most attention. Find out who are the 20% of your segment that bring the 80% of
revenue (refer to Pareto principle). Another way one can gather data is through
usability testing where one observes the behaviors of customers and records the
observations.

g) Outcome segmentation
here, you focus on different types of products that have been purchased, the number
of people who did not complete purchase, where most of your orders are coming from
etc. In short, the aim of segmentation is to find out where your most profitable
segment comes from and focus your efforts on that segment.
 Your hypothesis should have the following characteristics:
 It is goal oriented—it clearly states what needs to be accomplished
 It can be tested—it can easily be implemented
 It is insightful—looking at the hypothesis, one should learn something about the
problem.

An example of hypothesis

Problem: less than 5% of visitors buy the mobile app Hypothesis: The text in the CTA
button does not provide a clear message to the customer. The text needs to be changed from
“Get it” to “Download you app now”.
 h) Test substantial variations based on your Hypothesis

We can call this the brainstorming stage. After determining the problem and
articulating a hypothesis. The next thing that follows is coming up with
substantial variations based on your hypothesis. Taking the above example, the
hypothesis states that “The text in the CTA button does not provide a clear
message to the customer.” The substantial variations could include things like
changing the color of the button, changing position of the CTA on the landing
page, changing the wordings, creating different icon etc. The substantial
variations of your hypothesis are meant to bring you closer to the solution as
quickly as possible and provide you with insights.

i) Analyze results to validate your hypothesis and Repeat

Once you’ve managed to articulate your hypotheses and test substantial

variations, it’s time to analyze results to validate your hypothesis. You need to
have sufficient test results in order to analyze and compare. When you are
analyzing your tests with the aim of implementing solutions, you should bear in
mind that revenue is the ultimate measurement of improvement. Customer
feedback and analytics are tools you can use. You should look at the data your
customers have left to help you choose the elements that need to be analyzed. The
various elements you could test include:

 CTAs—colors, texts, size

 Images—placement size
 Headlines—size, length, style, tone text color
 Testimonials—placement, number, length
 Videos—number, with or without videos
 Forms—files type, color, number of fields
 Shopping cart—icon, text, number of steps
 Copywriting—long text or short, style, tone

After learning from your results, you should start the process all over because there is
always room for improvement. In marketing, you never hit the perfect solution and even there
is no way to know that you have reached perfection. Constant improvement is the name of
the game in this field. So, it is a continuous process.

Referensi : Steven Sanchez has been actively involved in SEO and Internet marketing
since 1999. Steven’s knowledge and experience have made him one of the most respected
and referenced SEO’s in the industry and his passion for innovation and growth has led his
company, Internet Marketing Invesp, to become one of the world’s leading online marketing
firms. (https://brand24.com/blog/7-steps-to-formulate-a-strong-hypothesis-for-your-next-ab-
test/)