statistics hypothesis testing what does hypothesis testing mean

A statement that expresses a population parameter's actual value as being less than, greater than, or not equal to the value given in the null hypothesis is known as an alternative hypot

NATIONAL ECONOMICS UNIVERSITY CENTER FOR ADVANCED EDUCATIONAL PROGRAMS

STATISTICS HYPOTHESIS TESTING

Table of Contents

1 What does Hypothesis testing means ? 4

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PART 1 INTRODUCTION

1 What does Hypothesis testing means ?

Researchers can evaluate a hypothesis’ plausibility using the technique known as hypothesis testing It entails determining whether or not an assumption on a particular population parameter is accurate Variance, standard deviation, and median are some

of these population metrics Typically, null hypothesis development comes first, followed by a number of tests that either confirm or reject the null hypothesis To compare the correlation or link between two or more variables, the researcher utilizes test statistics Additionally, researchers employ hypothesis testing to compute the coefficient of variation and establish the statistical significance of the regression relationship and correlation coefficient

2 What is this method solved ?

Testing hypotheses is essential The most significant advantage of hypothesis testing is that it enables you to evaluate the reliability of your claim or assumption before applying it to your data collection Additionally, the only reliable way to establish if something “is or is not" is through hypothesis testing Other advantages are:

1 Hypothesis testing offers a solid framework for deciding how to use data for your target audience

2 It makes successful extrapolation of data from the sample to a wider population possible for the researcher

3 The researcher can assess whether or not the data from the sample are statistically significant by using hypothesis testing

4 One of the most crucial techniques for gauging the accuracy and dependability

of findings in any systematic inquiry is hypothesis testing

5 Links to underlying theory and particular research questions are helpful

STEPS OF HYPOTHESIS TESTING

® Step 1: State the hypotheses

* Step 2: Set the criteria for a decision

¢ Step 3: Compute the test statistics

¢ Step 4: Make a decision

Step | : First, state your hypothesis We start out by presuming that the assertion or hypothesis we are testing is true The null hypothesis states this Whether this supposition is likely to be accurate serves as the foundation for the decision The null hypothesis (HO), sometimes known as the null, is a statement that assumes the truth of

a population parameter, such as the population mean We'll investigate the likelihood

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that the value given in the null hypothesis is accurate A statement that expresses a population parameter's actual value as being less than, greater than, or not equal to the value given in the null hypothesis is known as an alternative hypothesis (Ha) Step 2 : Establish the standards for judgment We declare the test's level of

significance in order to establish the standards for a choice Level of significance, also known as significance level, is a criterion of judgment used to determine whether to accept or reject the value claimed by a null hypothesis The criterion is based on the likelihood that a statistic would be measured in a sample if the null hypothesis’ value were accurate The threshold or degree of significance in behavioral research is often set at 5% We reject the value indicated in the null hypothesis when there is a less than 5% chance of obtaining a sample mean if the null hypothesis were true

Step 3: Do the test statistic computation A mathematical method called the test statistic enables researchers to calculate the probability of receiving sample results if the null hypothesis were to be true The null hypothesis is decided upon using the test statistic result (explained in Step 4)

Step 4 : Reach a conclusion To decide if the null hypothesis is true, we consider the test statistic's value The P value indicates the likelihood of finding a sample mean if the null hypothesis’ stated value is accurate The p value is a measure of probability that is always positive and ranges from 0 to | In Step 2, we specified the threshold or probability of finding a sample mean, which is commonly set at 5% in behavioral research, and at which point we will decide whether to reject the value specified in the null hypothesis As assess the p value against the standard we established in Step 2 to get a conclusion P-values represent the likelihood of receiving a sample result in the event that the null hypothesis’ value is correct The level of significance is compared

to the p value for achieving a sample outcome

In conclusion, a researcher has two options:

1 We reject the null hypothesis when the p value is less than 5% (p 05)

2 We maintain the null hypothesis when the p value is more than 5% (p >.05)

3 Application

1 Medical research

Research in medicine can also benefit from the use of hypothesis testing Let's say a pharmaceutical company wishes to assess the efficacy of a new medicine it has produced to treat a certain illness A group of patients with the disease can be used in a clinical trial, with half of the patients recetving the new medication and the other half receiving a placebo They can then use the following hypotheses to conduct a hypothesis test:

« Null Hypothesis (Ho): The new drug has no effect on the disease

« Alternative Hypothesis (Ha): The new drug is effective in treating the disease

If the p-value of the test is less than some significance level (e.g a = 05), then the pharmaceutical company can reject the null hypothesis and conclude that the new drug

is effective in treating the disease

2 Finance

In the area of finance, hypothesis testing is used yet again Consider an investor who thinks buying stocks in a certain sector will provide better returns than the general market They can gather information on the performance of the market as a whole as well as the returns of stocks in that industry over a specific time period to test this They can then use the following hypotheses to conduct a hypothesis test:

« Null Hypothesis (Ho): The returns of stocks in the industry are not significantly different from the market average

« Alternative Hypothesis (Ha): The returns of stocks in the industry are significantly higher than the market average

If the p-value of the test is less than some significance level (e.g a = 05), then the investor can reject the null hypothesis and conclude that investing in stocks in that industry will yield higher returns than the market average

3 Marketing

The field of marketing can also benefit from hypothesis testing For instance, a marketing team can think that a particular ad campaign will result in more product sales The following hypotheses can be used in a hypothesis test:

« Null Hypothesis (Ho): The advertisement has no effect on sales

« Alternative Hypothesis (Ha): The advertisement leads to increased sales

If the p-value of the test is less than some significance level (e.g a = 05), then the marketing team can reject the null hypothesis and conclude that the advertisement leads to increased sales

4 Real estate

Hypothesis testing can be used in the real estate sector to determine whether a particular property attribute impacts its worth For instance, a real estate agent might think that a swimming pool raises a property's value The following hypotheses can be used in a hypothesis test:

¢ Null Hypothesis (Ho): The presence of a swimming pool has no effect on the value of the property

« Alternative Hypothesis (Ha): The presence of a swimming pool increases the value of the property

If the p-value of the test is less than some significance level (e.g a = 05), then the real estate agent can reject the null hypothesis and conclude that having a swimming pool does increase the value of a property

5.

PART 2 ARTICLE SUMMARY

1 Article name

Comparison of Student Learning Outcomes Through Video Learning Media with Powerpoint

By Illa Mudasih1 , Waspodo Tjipto Subroto2

4 Case

The article discusses the importance of education for every human being to improve their quality of life and how technology has influenced the field of education in the era

of globalization It specifically focuses on the implementation of the 2013 Curriculum

in Indonesia and the use of learning media such as video and PowerPoint in teaching factory overhead material to class XII AK students

5 Purpose

The purpose of the article is to emphasize the importance of education, innovative and creative teaching and learning processes, and the use of learning media to improve student learning outcomes The study conducted by the researchers aims to determine the difference in learning outcomes between video learning media and PowerPoint in teaching factory overhead material to class XII] AK students

6 Technique

o Step 1: Collect data and method

This study uses experimental research with a type of True Experimental research Experimental research is a study that is used to find the effect of certain treatments on others in controlled conditions So, experimental research means finding the influence

of a variable that gets treatment The design carried out in this study was the design of the pretest-posttest control group design

The sample was randomly drawn from 2 out of 3 classes that were class XI] AK 1, XI

AK 2, and XI] AK 3 SMK Wachid Hasyim 2 Surabaya

The sample in this study consisted of students of:

- Class XII AK 1 with 36 female students and | male student as experimental class 1

- Class XII AK 2 with 37 all-female students as experimental class 2

Class Pre-test Treatment Post-test

o Step 2: Data analysis

The article explains various methods of data analysis, which include normality test, homogeneity test, and hypothesis test It highlights the significance of conducting normality and homogeneity tests before carrying out a hypothesis test

- The homogeneity test is used to determine whether the sample variance used

is homogeneous,

- The Chi-Square test is used for the normality test

- t-test used in hypothesis testing to check for differences in student learning outcomes between the control class and the experimental class.The SPSS 16 program

is employed for performing these tests

This study begins with pretesting in the experimental class and the control class to determine the student’s initial ability to factory overhead material After the pretest, the results of the pretest were tested for normality and homogenates as a prerequisite test of the test

Table 2 Test Results Normality

After testing the normality of the sample through the help of SPPS, the chi-square test for the value of the pretest video has a significance of 0.388 while the value of the pretest powerpoint has a significance of 0.059 Then the two significance levels are normally distributed because they are greater than 0.05

The homogeneity test was conducted to find out the two samples (experimental class and control class) used in the study had homogeneous or non-homogeneous variants The homogeneity test results for pretest data are as follows:

Levene Statistics dfl df2 Sig posttest Based on Mean 613 1 72 436

Based on median and

- - 360 1 71 861 551 with adjusted df

Based on the mean

It also shows that the sample used has the same or homogeneous variance

After knowing that both classes are homogeneous, they determined the sample class:

- Class XII Ak | as experimental class 1 (video media), average point pretest

Ho = There is no difference in student learning outcomes between classes given treatment using video learning media and classes given treatment using Powerpoint learning media

Ha = There are differences in student learning outcomes between classes given treatment using video learning media and classes given treatment using Powerpoint learning media

The average posttest of the experimental class 2 (PowerPoint media) was obtained by

79, while the average posttest of experimental class | (video media) was 85

Viewed from the results of the posttest in the experimental class | (media video) and experimental class 2 (media PowerPoint) there are differences, where the experimental class | (video media) gets an average posttest value higher than the average value of the posttest experimental class 2 (PowerPoint media) Of the differences in the results

of the posttest there are differences, but the differences cannot be said to be

significant With that, the Independent Sample T-Test statistical test is conducted The hypothesis test is as follows:

95% Confidence Interval of the Mean | Std Error Difference Sig (2- | Differenc | Differenc

F Sig f tailed) e e Lower Upper Hasi lbelajar assumed Equal variances 613 436) 349) 2 001[ 4.64865) 1.38797] 1.88178] 7.41552

Equal variances

not assumed 3491 1908 001[ 4.64865] 1.38797] 1.88172] 741558

Table 4 Hypothesis Test Results

The results of the statistical test with the Independent Sample T-test showed a significance level of 0.001 <0.05, indicating that the results of the t-test <significance level of 0.05

Therefore hypothesis Ho is rejected and Ha 1s accepted It was concluded that the learning outcomes of experimental group | using videos were different or not the same from the learning outcomes of the experimental group 2 students using PowerPoint media with a significant level of 0.05 or a confidence level of 95%

7 Conclusion

The aim of this study was to determine the differences in learning outcomes for class XII AK students in factory overhead material when using video media with PowerPoint Previous research by Fiona (2018) suggested that lower PowerPoint usage resulted in better learning outcomes compared to powtoon The study concluded that the experimental class 1 student who used video media had an average score of

85, while the experimental class 2 students who used PowerPoint media had an average score of 79 The results showed a significant difference in learning outcomes between the experimental classes, with a significant level of 0.001 This study was limited to the learning process of factory overhead costs Based on the findings, it is recommended that instructional media should be improved beyond just video and PowerPoint media to encourage students to learn Further research is required to develop more interesting media updates and address the limitations of this study

artificial neural network (ANN), support vector machines with polynomial

(SVMpoly) and support vector machines with radial basis function kernels

(SVMRBEF) This study also tested hypotheses about some controversial issues

related to price prediction in adition to other research methods In the scope of this report, we only mention the application of hypothesis testing theories in this study

© Hypothesis tested

- The prediction performances of each method (ANN, SVMpoly, SVMRBF) are similar

- Google Trends provides better prediction performances compared with the

prediction without this web search frequency

- The predictive performance of the whole market index is different from those

of the ensemble approaches with major companies in the index

© Method : p-value

® Processes and Conclusion

- The prediction performances of each method (ANN, SVMpoly, SVMRBEF) are similar

reject/fail to reject Hy reject reject reject

This table shows results of the first hypothesis test All methods reject the null hypothesis

Table 5 Results of the first hypothesis test

=> Interpretation: Machine-learning methods with general procedures do not perform well in predicting the trends of market index prices

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- Google Trends provides better prediction performances compared with the

prediction without this web search frequency

Table 6 Results of the second hypothesis test

=> Interpretation: Google Trends can be ineffective in predicting the index prices

- The predictive performance of the whole market index is different from those of the ensemble approaches with major companies in the index

reject/fail to reject Hy fail to reject fail to reject fail to reject

This table shows result of the third hypothesis test All methods fail to reject the null hypothesis

Table 7 Results of the third hypothesis test

=> Interpretation: The ensemble methodology’s effect on the directionality of the market index is unremarkable

a Confidence intervals rather than P values: estimation rather than

hypothesis testing

© Case

- Author: MARTIN J GARDNER, DOUGLAS G ALTMAN

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