Hypothesis Testing In Statistics | Hypothesis Testing Explained With Example | Simplilearn
Summary
TLDRThis video from Simply Learn explores the concept of hypothesis testing, a statistical method for validating claims about population parameters using sample data. It starts with the formulation of a hypothesis from a research question, emphasizing the difference between the two. The video delves into criteria for a good hypothesis, introduces the null and alternative hypotheses, and explains test statistics like t-tests, z-tests, and f-tests. It concludes with the significance level, using an example of students' performance with special learning aids to illustrate the process. The video aims to clarify these statistical concepts, encouraging viewers to engage with the content and subscribe for more.
Takeaways
- 🔍 Hypothesis testing is a statistical method used to test a claim about a population parameter using sample data.
- 📚 A research question is a specific concern derived from a broader research problem and guides the investigation.
- 💡 The hypothesis is a tentative statement predicting the relationship between variables, often starting with a question and supported by background research.
- 🚫 The null hypothesis (H₀) assumes that an event will not occur and is used as a basis for testing against the alternative hypothesis.
- 🌐 The alternative hypothesis is the logical opposite of the null hypothesis and is accepted if the null hypothesis is rejected.
- 📊 Test statistics summarize observed data into a single number to compare against the expected distribution under the null hypothesis.
- 📈 T-tests, Z-tests, and F-tests are common statistical tests used in hypothesis testing, each with specific applications and interpretations.
- 🎓 A hypothesis should be compatible with current knowledge, logically consistent, clearly stated, and testable.
- 📉 The significance level is a threshold for deciding whether the null hypothesis can be rejected; it is often set at 0.05 or 1%.
- 📚 An example given in the script involves testing the impact of special science learning videos on student performance in a competency test.
- 🔔 The video concludes by emphasizing the importance of hypothesis testing in research and encourages viewers to ask questions and subscribe for more content.
Q & A
What is hypothesis testing?
-Hypothesis testing, also known as significance testing, is a statistical method used to test a claim or hypothesis about a population parameter using sample data. It checks if there is sufficient statistical evidence to support the hypothesis claimed.
What is the purpose of hypothesis testing?
-The purpose of hypothesis testing is to determine whether there is enough statistical evidence to support a hypothesis, thus allowing researchers to make informed decisions about the validity of their claims.
What is the difference between a research question and a hypothesis?
-A research question is a specific concern that aims to be answered through research, derived from a broader research problem. A hypothesis, on the other hand, is a tentative statement about the relationship between variables, making predictions about experimental outcomes based on the research question.
What are the criteria for developing a good hypothesis?
-A good hypothesis should be compatible with current knowledge, follow logical consistency, be testable, and be stated briefly and clearly.
What is the null hypothesis?
-The null hypothesis (H0) is an assumption that there is no effect or relationship between variables being studied. It is used as a basis for comparison against the alternative hypothesis.
What is the alternative hypothesis?
-The alternative hypothesis is a logical opposite of the null hypothesis. It represents the research hypothesis that the researcher is trying to support and is accepted if the null hypothesis is rejected.
Can you explain the concept of test statistics in hypothesis testing?
-Test statistics is a number calculated from the statistical test of a hypothesis, indicating how closely the observed data matches the expected distribution under the null hypothesis. It summarizes the observed data into a single number using measures such as central tendency, variation, and sample size.
What are the different types of statistical tests mentioned in the script?
-The script mentions three types of statistical tests: t-test, z-test, and f-test. The t-test compares the means of two groups, the z-test compares a sample mean with a population mean when population variance is known or sample size is greater than 30, and the f-test assesses the equality of variances or the ratio of two variances.
What is the significance level in hypothesis testing?
-The significance level, often denoted as alpha (α), is the probability threshold used to decide whether to reject the null hypothesis. It is typically set at 0.05 (5%), indicating that if the probability of observing the data under the null hypothesis is less than this threshold, the null hypothesis is rejected.
How does the example in the script illustrate the concept of hypothesis testing?
-The example in the script involves a study on students receiving special learning aids through online science videos. The research hypothesis predicts that these students will score higher on a science competency test than those who did not receive the videos. The null hypothesis states that there is no impact of the videos on student scores. The significance level is used to determine whether to reject or support the null hypothesis based on the study results.
Outlines
🔍 Introduction to Hypothesis Testing
This paragraph introduces the concept of hypothesis testing, explaining it as a method for evaluating claims about a population parameter using sample data. It emphasizes the purpose of hypothesis testing, which is to determine if there is sufficient statistical evidence to support a hypothesis. The paragraph also distinguishes between a research question and a hypothesis, highlighting that a hypothesis predicts outcomes while a research question does not. It further outlines the criteria for a good hypothesis, such as compatibility with existing knowledge, logical consistency, clarity, and testability. The discussion then moves to the null hypothesis and alternative hypothesis, explaining their roles in hypothesis testing and providing an example related to the impact of advertisement duration on product sales.
📊 Exploring Test Statistics and Hypothesis Testing Methods
The second paragraph delves into the specifics of test statistics, which quantify how closely observed data aligns with the expected distribution under the null hypothesis. It outlines three main types of statistical tests: t-tests, which compare group means; z-tests, used when population variance is known or sample size exceeds 30; and f-tests, which assess the ratio of variances. The paragraph explains the null and alternative hypotheses associated with these tests, using the example of a study on the effectiveness of special learning aids in improving science test scores. It concludes by defining the significance level, a threshold for determining whether to reject the null hypothesis, often set at 0.05, and encourages viewers to engage with the channel for further learning opportunities.
Mindmap
Keywords
💡Hypothesis Testing
💡Research Question
💡Hypothesis
💡Null Hypthesis
💡Alternative Hypothesis
💡Test Statistics
💡T-test
💡Z-test
💡F-test
💡Significance Level
Highlights
Hypothesis testing is a method for testing a claim or hypothesis about a parameter in a population using data measured in a sample.
The purpose of hypothesis testing is to check if there is enough statistical evidence in favor of a hypothesis.
A research problem identifies a broad issue to address, while a research question is a specific concern to be answered through research.
A hypothesis makes a prediction about experimental outcomes, whereas a research question does not.
A hypothesis is a tentative statement about the relationship between variables, involving more than a guess and based on background research.
Good criteria for a hypothesis include compatibility with current knowledge, logical consistency, clarity, and testability.
The null hypothesis assumes that an event will not occur, symbolized by H₀.
The alternative hypothesis is the logical opposite of the null hypothesis and is accepted if the null hypothesis is rejected.
Test statistics summarize observed data into a single number to compare with the expected distribution under the null hypothesis.
T-tests are used to compare the means of two groups and determine if a process or treatment affects the population of interest.
Z-tests compare a sample mean with a population mean when either the population variance is known or the sample size is greater than 30.
F-tests assess the equality of variances and are flexible for various situations by changing the variances included in the ratio.
An example study investigates whether special science learning videos improve students' scores on a science competency test.
The significance level is a criterion for judgment based on the probability of obtaining a statistic if the null hypothesis were true.
A commonly set significance level is 0.05, which can be interpreted as a 5% probability threshold for rejecting the null hypothesis.
If the study result indicates a probability lower than the significance level, the null hypothesis can be rejected.
If the study result indicates a probability higher than the significance level, the null hypothesis is supported.
The video concludes with an invitation for questions, subscription, and notification activation for updates.
Transcripts
hi everyone welcome to this exciting
session by simply learn
today we have a really interesting topic
for you
in this video we'll be discussing about
the hypothesis testing
we'll start by talking about the
hypothesis testing and how a research
question can help you come with a good
hypothesis
then we'll look at the hypothesis and
the criteria for developing a good
hypothesis
following that we'll look at the null
hypothesis concept test statistics and
understand it with the help of an
example
finally we conclude this video by
briefing you about the significance
level
so let's get started
what is hypothesis testing
hypothesis testing or significance
testing is a method for testing a claim
or hypothesis about a parameter in a
population using data measured in a
sample
the purpose of the hypothesis testing is
to check if there is enough statistical
evidence in a favor of hypothesis that
we have claimed
now we will move on to the research
question
a research problem is a broad issue that
you would like to address through your
research
it identifies the difficult doubt or
area of concern in the theory or in
practice
that requires thought and investigation
research objectives are clear statements
of what you aim to achieve through your
research there are specific actions that
you will take and act as a milestone
that will help you complete your
research a research question is a
specific concern that you will answer
through research it is derived from a
research problem but is based on a study
design
when you narrow down on a research
problem to a specific idea that points
towards a feasible way to investigate or
address the research problems we'll get
a research question
now
what you should really take care of is
the slight difference between the
research question and the hypothesis
hypothesis makes prediction about
experimental outcome whereas research
question does not
let's understand with the help of an
example suppose your research question
is how many hours per day a student
spends on a giving and you want the
answer for that
another recent question may be
do the college students with more study
hours can achieve the higher gpa than
the students who do not put in more
hours for study
these questions can be answered by
framing a hypothesis so let's understand
what a hypothesis is
a hypothesis is a temptative statement
about the relationship between two or
more variables
it is a specific festival predictions
about what you expect to happen in a
study the hypothesis is a prediction but
it involves more than a guess
most of the time hypothesis begins with
a question which is then explored to a
background research
hypothesis translates the research
question into a prediction of expected
outcomes
unless you are creating an explanatory
study a hypothesis should always explain
what to expect to happen
so as in the previous slide we have
framed the question that how many hours
per student spend on a gaming related to
this question we can create a hypothesis
that students who spend more time on
gaming are socially alienated
so there are some good criteria for a
good hypothesis let's discuss what are
those
a hypothesis should be compatible with
the current knowledge in the area and it
should follow the logical consistency it
should not be inconsistent in places
a good hypothesis must be shaded briefly
and clearly
and it should be testable now we'll
understand what a null hypothesis and
alternative hypothesis is
the null hypothesis is the assumption
that an event will not occur
a null hypothesis has no bearing on the
study's outcome unless it is rejected
h naught is a symbol for null hypothesis
the alternating hypothesis or a research
hypothesis is a logical opposite of the
null hypothesis
the acceptance of the alternative
hypothesis follows the rejection of the
null hypothesis
let's understand the null hypothesis
with the help of an example suppose we
have a research hypothesis the duration
of an advertisement on the tv channels
is positively related to the sales of
the product
the more the duration the more the sales
of that product
so the null hypothesis of this
assumption would be
the duration of the advertisement on the
tv channels are not related to the sales
of the product they're not related at
all now we'll move on to the test
statistics that i frequently discussed
when creating hypothesis testing
the test statistics is a number
calculated from the statistical test of
a hypothesis it shows how close your
observed data matches the distribution
expected under the null hypothesis of
data statistical test
the distribution of the data is how
often each observation occurs and can be
described by central tendency and
variation around the central tendencies
the test statistics summarizes your
observed data into a single number using
the central tendency variation sample
size and the other number of predictable
variables in your statistical model
there are broadly three capsule
statistical tests
different statistical tests have
slightly different ways of calculating
these test statistics but the underlying
hypothesis and interpretation of the
test statistics stay the same
so the first one is a t-test
a t-test is a statistical test that is
used to compare the means of two groups
it is often used in hypothesis testing
to determine whether a process or
treatment actually has an effect on the
population of interest or whether the
two groups are different from one
another the null hypothesis
is that the true difference between
these group means is zero and the
alternative hypothesis is that the true
difference is different from zero
now let's move on to the test
the tests are statistical way of testing
a hypothesis that either we know the
population variance or we don't know the
population variance but our sample size
is greater than 30. we perform the z
test when we want to compare a sample
mean with the population mean
the next is an f test the f test or the
f statistics is simply a ratio of two
variances
variances is a measure of dispersion or
how far the data are scattered from the
mean larger values represent a greater
dispersion despite being a ratio of
variances you can use a f-test in a wide
variety of situation
f-test can assess the equality of
variances however by changing the
variances that are included in the ratio
the f-test became a very flexible test
now that we have discussed all the
theories related to the hypothesis
testing let's take an example to
understand them more clearly
consider a study on a group of students
whether students receive a special
learning aid to the online videos on
science
it is hypothesized that after viewing
these online videos the group of
students who are better than the one who
have not seen the videos
so in this case a research hypothesis
will be students who have received their
special science learning videos will
score high in a science competency test
and its null hypothesis will be the
special science learning videos may no
impact on the scores of the students
now we will discuss what a significance
level is
level of significance or significance
level refers to a criterion of judgment
upon which a decision is made regarding
the values stated in the null hypothesis
this criteria is based on the
probability of obtaining a statistic
measured in a sample if the values
stated in null hypothesis were true
generally the criterion of the level of
significance is typically set at 5
or 0.05
this value of significance level can
also be taken as one percent or 10
depending upon your requirement
if the result obtained from the study
indicates the probability lower than the
significance level so in this case a
researcher can reject null hypothesis
otherwise if the result of the study
indicates a probability higher than the
significance level the researcher can
support the null hypothesis with that we
have come to the end of this video on
hypothesis testing i hope this was
helpful
if you have any questions then please
put them in the comment section also
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