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According to the American Psychological Association, about twenty thousand new psychological tests are developed annually. Still, with numerous similar tools available, psychologists must clearly understand the proposition and reason for Testing in any situation. The testing proposition differs from the cerebral propositions of personality intelligence, and exploration shows that a psychologist makes about twelve assumptions in the testing process. The hypotheticals aim to create cerebral tests, establish their theoretical frame, and determine how the interpreted result will be employed in a given setting.

What is Testing or Assessment?

Psychological tests aid in identifying mental problems in a standardized, reliable, and valid manner. A diagnosis can be made using a variety of tests. Psychological evaluation is gathering information about people and applying it to make key predictions and conclusions about their cognition and personality. Psychological exams are used to examine psychological qualities. A psychological exam is simply an objective and standardized evaluation of a sample of behavior. Psychological tests are similar to other scientific tests in that observations are performed on a limited but carefully chosen sample of an individual’s behavior. In this regard, the psychologist works similarly to the biochemist who analyses a patient’s blood.

Psychological tests have a wide range of applications and are utilized in various settings, including therapeutic, counseling, industrial, and organizational settings and forensic settings. It can be used to diagnose psychiatric illnesses in a therapeutic setting, and Beck’s depression inventory, for example, can aid in diagnosing depression.

It may be utilized in counseling to make career selections and understand one’s aptitude and interests. In this context, tests such as the Differential Aptitude Test, Career Preference Record, and Vocational Interest Inventory can be employed. Psychological examinations may also be utilized in industrial and organizational settings for employee selection and to analyze stress-related difficulties, among other things.

In this configuration, job stress scales, organizational citizenship behavior, job satisfaction scales, and so on can be employed. Psychological tests can also be used in forensic psychology to determine an individual’s psychological condition. Thus, psychological tests may be used to assess a variety of psychological entities such as intellect, personality, creativity, interest, aptitude, attitude values, and so on. Psychological tests also assess internet addiction, resilience, mental health, psychological well-being, perceived parental behavior, family environment, and so on.

Why are the Assumptions about Psychological Testing?

Tests aiming to reflect the literacy aptitudes of academy children differ much further than generally is honored. Still, error in assessing similar literacy aptitude inheres much further in the users of the tests than in the tests themselves. Hypotheticals abecedarian to similar assessment, or indeed Testing, are considered. It is particularly important that the assessor, or tester, constantly be sensitive to the relationship between the cerebral demands of test particulars or tests and the literacy demands defying the child.

Indeed, tests that generally are grossly or crudely used can yield psycho-educationally meaningful information if their results are differentially perceived in terms of the light they throw on the cerebral operations abecedarian to literacy, “process,” as varied with that thrown on the results of the functioning of similar operations, “product.”

Assumptions of Psychological Testing and Assessment given by APA

Assumption 1 − Psychological traits and states exist. The trait has been defined as “any distinguishable, fairly enduring way in which one existent varies from another.” States distinguish one person from another but are fairly less continuing. Cerebral particularity covers a wide range of possible characteristics.

Construct is an informed, scientific conception developed or constructed to describe or explain behavior.

Overt conduct refers to an observable action or the product of an observable action, including test- or assessment-related responses.

The delineations of traits and countries we use also relate to how one existence varies.

Assumption 2 − Psychological traits and states can be quantified and measured. Having admitted that cerebral traits and countries do live, the specific traits and countries to be measured and quantified need to be precisely defined.

Assumption 3 − Various approaches to measuring aspects of the same thing can be useful. Decades of court challenges to various tests and testing programs have acclimatized test inventors and druggies to the societal demand for fair tests used fairly. Moment, all major test publishers strive to develop fair instruments when used in strict agreement with guidelines in the tested primer. Test tools are just like other tools; they can be used duly or inaptly.

Assumption 4 − Assessment can give answers to some of life’s most meaningful questions. Considering the numerous critical opinions grounded on testing and assessment procedures, we can readily appreciate the need for tests, especially good ones

Assumption 5 − Assessment can pinpoint marvels that bear further attention or study.

Assumption 6 − A variety of sources of data enrich and are part of the assessment process.

Assumption 7 − Various sources of error are part of the assessment process. Error traditionally refers to a commodity further than anticipated; it is an element of the dimension process. More specifically, error refers to a long-standing supposition that factors other than what a test attempts to measure will impact performance on the test.

Assumption 8 − Tests and Other dimension ways Have Strengths and weakness. Competent test users understand a great deal about the tests they use. For example, they understand, among other effects, how a test was developed and the circumstances under which it is applicable to administer the test. Likewise, competent test users understand and appreciate the tests’ limitations and how those limitations might be compensated for data from other sources.

Assumption 9 − Test-affiliated conduct predicts non-test-related conduct. Patterns of answers to true-false questions on one extensively used test of personality are used in decision timber regarding internal diseases. The task in some tests mimics the factual actions that the test users are trying to understand. For example, the attained conduct sample is used to diagnose unborn behavior.

Assumption 10 − Present-day conduct slice predicts unborn conduct.


Psychological testing is defined as the administration of psychological tests. Psychological tests measure IQ, personality, attitude, interest, accomplishment, motivation, and so on. They may be defined as the standardized and objective measurement of a sample of behavior. Psychological testing is mostly objective, and they are also predictive and diagnostic. A psychological exam is also standardized, which means that the technique for conducting and evaluating the test is consistent.

When it comes to psychological testing, there are several assumptions. In psychological testing, there are four introductory hypotheticals people differ in important trait; we can quantify these traits; the traits are nicely stable; and measures of the traits relate to factual behavior. With quantification, it has meant that objects can be arranged along a continuum. This quantification supposition is pivotal to the conception of measuring.

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Elements Of Psychological Testing Construction Administration Interpretation

Psychological testing is the systematic procedure of keeping track of an individual’s behaviour. Examples may include aptitude tests, intelligence tests, vocational tests, aptitude tests, personality tests, and many more. These tests are mostly designed to measure the difference between the abilities and capabilities of the test takers. In other words, it evaluates whether the mental abilities of the test taker are better or inferior to an average performing test taker. Trained evaluators and psychologists often administer these tests to diagnose individuals. In addition to that, these psychological tests also measure the difference in abilities of an individual over some time.

Based on its characteristics, the psychometric tests will be ideal for job screening, psychological diagnosis, academic placement, research purposes, etc. Construction of such tests requires elaborate planning to serve the purpose for which they have been designed. For example, the Minnesota Multiphasic Personality Inventory (MMPI-2) is one of the world’s most commonly used psychometric tests. It is ideal for measuring any mental health-related ailments.

Psychological Test Construction, Administration, and Interpretation

It can be studied under the following headings −

Psychometrics (Psychological Test Construction) 

Psychometrics, also known as psychological test construction, is the systemic use of tests (both verbal and written) used to qualify the different mental abilities of a test taker.

Test Administration

Test administration may be defined as the physical or psychological setting in which the psychological test takes place. The five factors that are involved in the test administration are −






Interpretation in psychology

Interpretation in psychology may be defined as incorporating an alternate frame of reference into the observation results of the collected data to make the results more amenable to manipulation.

Test Construction

To make sure they are accurate and trustworthy measurements of the construct they are designed to examine, psychological tests are created using a strict and organized approach. This entails determining the construct or trait that will be assessed, choosing test items that are pertinent and appropriate for the construct, pilot testing the items, and refining the test depending on the information gathered during the pilot testing. The development of tests must also take into account demographic and cultural aspects that could affect the test outcomes.

After the test is created, it must be normed using a sample of the population that is typical of the entire population to establish a standard for scores. It is crucial to remember that psychological tests do have limitations, and the findings should only be evaluated by qualified experts in the field who can take the subject’s particular traits and upbringing into account. To better comprehend the results, test items are frequently normed using a representative sample of the population and their scores are compared with the norm.


To construct a psychometric test, the test’s objectivity must be considered. The objectivity of a test is the degree to which equally competent test takers’ scores obtain the same results. It is the most important characteristic of a good test.


The appropriate age range, educational level, and cultural background of the average test takers must be determined beforehand before designing the test.


It is necessary to determine the content of the test, and the content of the test depends upon its objectivity.

Test Format

Multiple-choice questions, true-false questions, and inventive responses are different test formats. The designer of the test needs to decide the format of the test. A psychometric test can be brief and easy, and lengthy and difficult questions in a psychometric test can tire out the test taker. Untrue answers (or not well-thought-out answers) to the test questions might make it easier for the trained evaluators to correctly assess the behaviours or other mental characteristics of the test taker.

Test Instructions

The psychometric tests can be broadly classified into oral and written tests. The delivery of the test answers is relevant to the diagnosis.

Test Administration

Test administration may be defined as the physical or psychological setting in which the psychological test takes place. Hence, a detailed agreement for the preliminary and the final test must be considered.

Defining the User

To avoid confusion, the prerequisites for administering and interpreting the tests must be predetermined.

Test Duration 

The test’s probable length and duration must be decided beforehand.

Methods of Sampling

Sampling methods, whether it is going to be random or selective, must be predetermined.

Ethical and Social Considerations

The potential harm from administering the psychological tests must be mentioned in the guidelines. In addition to that, any safeguards built into the recommended testing procedure must be mentioned as well in order to prevent any harm.

Test Administration

A test’s administration will alter depending on whether it is administered. Individual or group testing is possible. When delivered to an individual, it will only be given to one person. Hence proper preparations must be done. Moreover, suitable seating arrangements and preparations are required if it is administered to a group. In any case, keep in mind that the test is done methodically and standardized. The test taker must follow the same instructions (as indicated in the handbook) (s). Before administering the exam to an individual or a group, be sure that all of the necessary materials are available. . Also, make sure that the location in which the exam will be given is favorable and devoid of distractions. You must thoroughly study the exam handbook and understand the concept that the test measures. When delivering a test, the following factors must be considered −

Concentrate on the reason for administering the test. Tests are given to individuals in order to assess them on specific variables. A test must also be chosen based on what is to be measured. Personality, for example, may be assessed using the 16 PF and the NEO-PI. You must choose a test based on what you intend to measure. A psychological test should be administered by someone who has the necessary qualifications, expertise, and skills (for the practical component of this course, the variables to be measured have already been specified, and you will learn how to administer and score a test measuring one of the specified variables).

Aside from information regarding the instructions, reliability, and validity, the handbook will often provide background information about the variable that the test measures. Details on how to score the test and norms will be supplied. Before administering the test, ensure that all necessary materials are available.

Concentrate on the ethical considerations that must be addressed during a test administration, such as secrecy, privacy, and so on. We briefly mentioned ethics in the first unit, which is also pertinent in this chúng tôi test taker must deliver the instructions when the exam is being administered (s). Also, appropriate clarification/answers must be provided if there are any uncertainties or inquiries.

While administering the test, keep the individual features in mind. When administering a test to youngsters, the elderly, persons with disabilities, and so on, your method may change. Language and culture must also be prioritized. The most crucial aspect of delivering a test is developing a relationship with the test takers so that their test anxiety is lessened and they cooperate fully during the test administration procedure.

If debriefing is necessary after the exam, any material not provided before the test is conducted must be communicated to the test taker(s). The exam taker might also provide an introspective report(s).

Scoring and Interpretation of the Psychological Tests

There are two types of scores Percentile scores and raw scores. In the case of an aptitude test, a raw score is the total number of questions that are correctly answered. If the test has been scored compared to other test takers, it will fall under the category of percentile score. In the case of an aptitude test, 100% is considered a perfect score, 90% is considered excellent, 80% is considered above average, and below 80% is considered average. The minimum accepted score is 40%. According to NCBI, the emotional intelligence score ranges from 27 and 108 for medical students. A score below or equal to 47 is considered “high emotional intelligence,” a score between 47 and 58 is considered “average emotional intelligence,” while that above 58 is “Below average emotional intelligence.”

Hand Scoring − Hand scoring is ideal when only a small number of test results are involved, and it takes much time. Hence, it can be considered more accurate.

Machine Scoring − Machine scoring is ideal for the times when a large number of test results are involved. Scoring takes place with the help of a computer-assigned scoring system.


Psychological testing is the systematic procedure of keeping track of an individual’s behavior. Trained evaluators and psychologists often administer these tests to diagnose individuals. These tests are mostly designed to measure the difference between the abilities and capabilities of the test takers and the difference in abilities of an individual over some time. A test administration may be defined as the physical or psychological setting in which the psychological test takes place. Construction of such tests requires elaborate planning so that they can serve the purpose for which they have been designed. Interpretation of the test results may depend upon the test results (which can be broadly classified into two: percentile scores and raw scores) and the objectivity (frame of reference).

What Is Stress Testing In Software Testing?

Stress Testing

Stress Testing is a type of software testing that verifies stability & reliability of software application. The goal of Stress testing is measuring software on its robustness and error handling capabilities under extremely heavy load conditions and ensuring that software doesn’t crash under crunch situations. It even tests beyond normal operating points and evaluates how software works under extreme conditions.

In Software Engineering, Stress Testing is also known as Endurance Testing. Under Stress Testing, AUT is be stressed for a short period of time to know its withstanding capacity. A most prominent use of stress testing is to determine the limit, at which the system or software or hardware breaks. It also checks whether the system demonstrates effective error management under extreme conditions.

The application under testing will be stressed when 5GB data is copied from the website and pasted in notepad. Notepad is under stress and gives ‘Not Responded’ error message.

Need for Stress Testing

Consider the following real-time examples where we can discover the usage of Stress Testing-

During festival time, an online shopping site may witness a spike in traffic, or when it announces a sale.

When a blog is mentioned in a leading newspaper, it experiences a sudden surge in traffic.

It is imperative to perform Stress Testing to accommodate such abnormal traffic spikes. Failure to accommodate this sudden traffic may result in loss of revenue and repute.

Stress testing is also extremely valuable for the following reasons:

To check whether the system works under abnormal conditions.

Displaying appropriate error message when the system is under stress.

System failure under extreme conditions could result in enormous revenue loss

It is better to be prepared for extreme conditions by executing Stress Testing.

Goals of Stress Testing

The goal of stress testing is to analyze the behavior of the system after a failure. For stress testing to be successful, a system should display an appropriate error message while it is under extreme conditions.

To conduct Stress Testing, sometimes, massive data sets may be used which may get lost during Stress Testing. Testers should not lose this security-related data while doing stress testing.

The main purpose of stress testing is to make sure that the system recovers after failure which is called as recoverability.

Load Testing Vs Stress Testing

Load Testing Stress Testing

Load Testing is to test the system behavior under normal workload conditions, and it is just testing or simulating with the actual workload Stress testing is to test the system behavior under extreme conditions and is carried out till the system failure.

Load testing does not break the system stress testing tries to break the system by testing with overwhelming data or resources.

Types of Stress Testing:

Following are the types of stress testing and are explained as follows:

Distributed Stress Testing:

In distributed client-server systems, testing is done across all clients from the server. The role of stress server is to distribute a set of stress tests to all stress clients and track on the status of the client. After the client contacts the server, the server adds the name of the client and starts sending data for testing.

Meanwhile, client machines send signal or heartbeat that it is connected with the server. If the server does not receive any signals from the client machine, it needs to be investigated further for debugging. From the figure, a server can connect with the 2 clients (Client1 and Client2), but it cannot send or receive a signal from Client 3 & 4.

Night run is the best option to run these stress testing scenarios. Large server farms need a more efficient method for determining which computers have had stress failures that need to be investigated.

Application Stress Testing:

This testing concentrate on finding defects related to data locking and blocking, network issues and performance bottlenecks in an application.

Transactional Stress Testing:

It does stress testing on one or more transactions between two or more applications. It is used for fine-tuning & optimizing the system.

Systemic Stress Testing:

This is integrated stress testing which can be tested across multiple systems running on the same server. It is used to find defects where one application data blocks another application.

Exploratory Stress Testing:

This is one of the types of stress testing which is used to test the system with unusual parameters or conditions that are unlikely to occur in a real scenario. It is used to find defects in unexpected scenarios like

A large number of users logged at the same time

If a virus scanner started in all machines simultaneously

If Database has gone offline when it is accessed from a website,

When a large volume of data is inserted to the database simultaneously

How to do Stress Testing?

Stress Testing process can be done in 5 major steps:

Step 1) Planning the Stress Test: Here you gather the system data, analyze the system, define the stress test goals

Step 2) Create Automation Scripts: In this phase, you create the Stress testing automation scripts, generate the test data for the stress scenarios.

Step 3) Script Execution: In this stage, you run the Stress testing automation scripts and store the stress results.

Step 4) Results Analysis: In this stage, you analyze the Stress Test results and identify bottlenecks.

Step 5) Tweaking and Optimization: In this stage, you fine-tune the system, change configurations, optimize the code with goal meet the desired benchmark.

Lastly, you again run the entire cycle to determine that the tweaks have produced the desired results. For example, it’s not unusual to have to 3 to 4 cycles of the Stress Testing process to achieve the performance goals

Tools recommended for Stress Testing

LoadRunner from HP is a widely-used Load Testing tool. Load Test Results shaped by Loadrunner are considered as a benchmark.

Jmeter is an Open Source testing tool. It is a pure Java application for stress and Performance Testing. Jmeter is intended to cover types of tests like load, functional, stress, etc. It needs JDK 5 or higher to function.

Stress Tester

This tool provides extensive analysis of the web application performance, provides results in graphical format, and it is extremely easy to use. No high-level scripting is required and gives a good return on investment.

Neo load

This is a popular tool available in the market to test the web and Mobile applications. This tool can simulate thousands of users in order to evaluate the application performance under load and analyze the response times. It also supports Cloud-integrated – performance, load and stress testing. It is easy to use, cost-effective and provides good scalability.

Metrics for Stress Testing

Metrics help in evaluating a System’s performance and generally studied at the end of Stress Test. Commonly used metrics are –

Measuring Scalability & Performance

Pages per Second: Measures how many pages have been requested / Second

Throughput: Basic Metric – Response data size/Second

Rounds: Number of times test scenarios have been planned Versus Number of times a client has executed

Application Response

Hit time: Average time to retrieve an image or a page

Time to the first byte: Time is taken to return the first byte of data or information

Page Time: Time is taken to retrieve all the information in a page


Failed Connections: Number of failed connections refused by the client (Weak Signal)

Failed Rounds: Number of rounds it gets failed

Failed Hits: Number of failed attempts done by the system (Broken links or unseen images)


Stress testing’s objective is to check the system under extreme conditions. It monitors system resources such as Memory, processor, network etc., and checks the ability of the system to recover back to normal status. It checks whether the system displays appropriate error messages while under stress.

Example of Stress Testing

E-commerce website announces a festival sale

News website at the time of some major events

Education Board’s result website

Social networking sites or blogs, apps, etc

Boundary Value Analysis And Equivalence Partitioning Testing

Practically, due to time and budget considerations, it is not possible to perform exhausting testing for each set of test data, especially when there is a large pool of input combinations.

We need an easy way or special techniques that can select test cases intelligently from the pool of test-case, such that all test scenarios are covered. We use two techniques – Equivalence Partitioning & Boundary Value Analysis testing techniques to achieve this.

What is Boundary Testing?

Boundary testing is the process of testing between extreme ends or boundaries between partitions of the input values.

So these extreme ends like Start- End, Lower- Upper, Maximum-Minimum, Just Inside-Just Outside values are called boundary values and the testing is called “boundary testing”.

The basic idea in normal boundary value testing is to select input variable values at their:


Just above the minimum

A nominal value

Just below the maximum


In Boundary Testing, Equivalence Class Partitioning plays a good role

Boundary Testing comes after the Equivalence Class Partitioning.

Equivalence Partitioning or Equivalence Class Partitioning is type of black box testing technique which can be applied to all levels of software testing like unit, integration, system, etc. In this technique, input data units are divided into equivalent partitions that can be used to derive test cases which reduces time required for testing because of small number of test cases.

It divides the input data of software into different equivalence data classes.

You can apply this technique, where there is a range in the input field.

Example 1: Equivalence and Boundary Value

Let’s consider the behavior of Order Pizza Text Box Below

Pizza values 1 to 10 is considered valid. A success message is shown.

While value 11 to 99 are considered invalid for order and an error message will appear, “Only 10 Pizza can be ordered”

Order Pizza:

Here is the test condition

Any Number greater than 10 entered in the Order Pizza field(let say 11) is considered invalid.

Any Number less than 1 that is 0 or below, then it is considered invalid.

Numbers 1 to 10 are considered valid

Any 3 Digit Number say -100 is invalid.

We cannot test all the possible values because if done, the number of test cases will be more than 100. To address this problem, we use equivalence partitioning hypothesis where we divide the possible values of tickets into groups or sets as shown below where the system behavior can be considered the same.

The divided sets are called Equivalence Partitions or Equivalence Classes. Then we pick only one value from each partition for testing. The hypothesis behind this technique is that if one condition/value in a partition passes all others will also pass. Likewise, if one condition in a partition fails, all other conditions in that partition will fail.

Boundary Value Analysis– in Boundary Value Analysis, you test boundaries between equivalence partitions

In our earlier equivalence partitioning example, instead of checking one value for each partition, you will check the values at the partitions like 0, 1, 10, 11 and so on. As you may observe, you test values at both valid and invalid boundaries. Boundary Value Analysis is also called range checking.

Equivalence partitioning and boundary value analysis(BVA) are closely related and can be used together at all levels of testing.

Example 2: Equivalence and Boundary Value

Following password field accepts minimum 6 characters and maximum 10 characters

That means results for values in partitions 0-5, 6-10, 11-14 should be equivalent

Enter Password:

Test Scenario # Test Scenario Description Expected Outcome

1 Enter 0 to 5 characters in password field System should not accept

2 Enter 6 to 10 characters in password field System should accept

3 Enter 11 to 14 character in password field System should not accept

Examples 3: Input Box should accept the Number 1 to 10

Here we will see the Boundary Value Test Cases

Test Scenario Description Expected Outcome

Boundary Value = 0 System should NOT accept

Boundary Value = 1 System should accept

Boundary Value = 2 System should accept

Boundary Value = 9 System should accept

Boundary Value = 10 System should accept

Boundary Value = 11 System should NOT accept

Why Equivalence & Boundary Analysis Testing

This testing is used to reduce a very large number of test cases to manageable chunks.

Very clear guidelines on determining test cases without compromising on the effectiveness of testing.

Appropriate for calculation-intensive applications with a large number of variables/inputs

Boundary Value Analysis and Equivalence Partitioning Testing Video


Boundary Analysis testing is used when practically it is impossible to test a large pool of test cases individually

Two techniques – Boundary value analysis and equivalence partitioning testing techniques are used

In Equivalence Partitioning, first, you divide a set of test condition into a partition that can be considered.

In Boundary Value Analysis you then test boundaries between equivalence partitions

Appropriate for calculation-intensive applications with variables that represent physical quantities

10 Mistakes To Avoid In Usability Testing

Better usability testing for websites and apps: the top 10 mistakes to avoid

Done well, usability testing enables organisations to validate ideas and designs for websites and applications with real-world users, helping increase engagement, satisfaction and conversions. But executing a successful user testing strategy brings with it numerous challenges and potential hazards. Think about your redesign and conversion optimisation projects. Are you at risk of committing any common testing mistakes?

Introduction to the value of usability testing

Research from the IEEE has found that fixing problems with software once development’s already begun is twice as expensive as resolving them before development commences.

Conducting lab-based usability testing that provides valuable insight into how users perceive and interact with your site or application before your project goes live can, therefore, deliver significant returns; in fact, it’s been found that for every dollar a company invests to increase usability it receives $10-$100 in benefits, and wins customer satisfaction and loyalty.

Ten most common mistakes in usability testing

However, successfully crafting, conducting and analysing these tests requires careful planning, and there are a number of challenges that you may stumble across. Here are some of the most common mistakes people fall victim to, along with tips to ensure you get the most out of any testing activity you undertake.

1. Recruiting unsuitable participants

2. Not testing early and often during the project lifecycle

Testing throughout the project lifecycle is therefore recommended and there are, in fact, a wide variety of methods available, meaning that whatever your requirements there’s an option to suit you. Here’s a brief comparison of some of the most popular approaches:





Guerrilla testing * Asking members of the public to complete tasks. Low cost. High number of tests Quantitative & qualitative results. Unpredictable

Unmoderated remote testing ** Participants have no contact with a facilitator and complete tasks in their home environment. Low cost. High number of tests Quantitative results. Slightly unpredictable

Moderated remote testing *** Participants test remotely. Sessions are run by a facilitator. Similar costs to office-based testing. Sessions can be watched live Qualitative results. Heavily reliant on internet connection

Laboratory testing **** Participants test in the office and sessions are run by a facilitator. They can be watched live Qualitative results. Most likely to have more statistical validity

A combination of qualitative and quantitative testing throughout the project lifecycle has been found to produce the best results, so think about how you will build this into your own project. For example, a recent project I was involved with tested:

HTML Prototype



Early draft 5 facilitated sessions Laptop, web cam, meeting room on client’s premises

Version 2 10 unmoderated sessions 3rd party testing solution

Version 3 5 facilitated sessions Box UK Usability Lab, observer at Box UK’s London office

Preparing a test plan is vital to keep track of sessions and, perhaps more importantly, guarantee that all important areas have been covered during the testing.

However, you shouldn’t be afraid to go off-course when necessary, as you may find that the answers you get when users are acting freely provide interesting insights you hadn’t considered previously.

Ranking your questions in order of importance can help you manage this unplanned activity while ensuring that critical topics are addressed.

4. Not rehearsing your setup

5. Using a one-way mirror

Many usability labs feature one-way mirrors that enable observers to monitor sessions without being seen by participants. However if the user knows they are being watched this can lead to unnatural behaviours and, subsequently, have a negative effect on your results.

For this reason, transmitting audio and visual feeds to a different room is often a better option, and one that also caters to remote teams anywhere in the world.

6. Not meeting participants in reception

It’s important to remember that participants may be nervous about the session ahead, particularly if they’ve never been involved in laboratory testing before and are unsure about what’s required. Greeting them as soon as they arrive and making them feel at home helps create a relaxed and comfortable environment to support an open and insightful session.

7. Asking leading questions

When creating your test plan, make sure you review all questions for any possible bias, as otherwise you may find that you’re actually leading the user to certain answers or actions based on what you expect (or want) to happen. Also be mindful of body language, as this can provide you with an even greater understanding of user responses.

8. Interrupting the participant

While test facilitators should support the participant and, as covered above, explore potentially valuable new areas of enquiry, the main purpose of usability testing is to gather feedback from representative end-users. As such, make sure you give them enough time to express their thoughts, and avoid interrupting until you’re sure they are finished.

9. Undertaking two roles in a testing session

Lab-based usability testing sessions involve both a facilitator who guides the user through the tasks, and an observer responsible for making notes and conducting the initial analysis.

If a single consultant is performing both these roles then critical responses could be overlooked, so be sure to allocate appropriate time and budget to testing.

10. Not considering external influences

Make Model Training And Testing Easier With Multitrain

This article was published as a part of the Data Science Blogathon.


For data scientists and machine learning engineers, developing and testing machine learning models may take a lot of time. For instance, you would need to write a few lines of code, wait for each model to run, and then go on to the following five models to train and test. This may be rather tedious. However, when I encountered a similar issue, I became so frustrated that I began to devise a way to make things simpler for myself. After four months of hard work – coding, and bug-fixing, I’m happy to share my solution with you.

MultiTrain is a Python module I created that allows you to train many machine learning models on the same dataset to analyze performance and choose the best models. The content of this article will show you how to utilize MultiTrain for a basic regression problem. Please visit here to learn how to use it for classification problems.

Now, code alongside me as we simplify the model training and testing of machine learning models with MultiTrain.

Importing Libraries and Dataset

Today, we will be testing which machine learning model would work best for the productivity prediction of garment employees. The dataset is available here on Kaggle for download.

To start working on our dataset, we need to import some libraries.

To import our dataset, you must ensure it is in the same path as your ipynb file, or else you will have to set a file path.

Python Code:

Now that we have imported our dataset into our Jupyter notebook, we want to see the first five rows of the dataset. You need to use a single line of code for that.

Note: Not all columns are shown here; only a few we would be working with are present in this snapshot except the output column.

Data Preprocessing

We wouldn’t be employing any primary data preprocessing techniques or EDA as our focus is on how to train and test lots of models at once using the MultiTrain library. I strongly encourage you to perform some major preprocessing techniques on your dataset, as dirty data can affect your model’s predictions. It also affects the performance of machine learning algorithms.

While you check the first five rows, you should find a column named “department”, in which sewing is spelt as sweing. We can fix this spelling mistake with this line of code.

df["department"] = df["department"].str.replace('sweing', 'sewing')

We can see in this snapshot above that the spelling mistake is now corrected.

When you run the following lines of code, you will discover that the “department” column has some duplicate values that we have to take care of, and we will also need to fix that before we can start predicting.

print(f'Unique Values in Department before cleaning: {df.department.unique()}') Output: Unique Values in Department before cleaning: ['sewing' 'finishing ' 'finishing']

To fix this problem:

df['department'] = df.department.str.strip() print(f'Unique Values in Department after cleaning: {df.department.unique()}') Output: Unique Values in Department before cleaning: ['sewing', 'finishing']

Let’s replace all missing values in our dataset with an integer value of 0

for i in df.columns:     if df[i].isnull().sum() != 0:         df[i] = df[i].fillna(0)

As I mentioned previously, we would use a label encoder for this tutorial to encode our categorical columns. Firstly, we have to get a list of our categorical columns. We can do that using the following lines of code.

cat_col = [] num_col = [] for i in df.columns:     if df[i].dtypes == object:         cat_col.append(i)     else:         num_col.append(i) #remove the target columns num_col.remove('actual_productivity')

Now that we have gotten a list of our categorical columns inside the cat_col variable. We can now apply our label encoder to it to encode the categorical data into numerical data.

label = LabelEncoder()     for i in cat_col:         df[i] = label.fit_transform(df[i])

All missing values formerly indicated by NaN in column ‘wip’ has now changed to 0 and the three categorical columns – quarter, department, and day have all been label encoded.

You may still need to fix outliers in the dataset and do some feature engineering on your own.

Model Training

Before we can begin model training, we will need to split our dataset into its training features and labels.

features = df.drop('actual_productivity', axis=1) labels = df['actual_productivity']

Now, we would need to split the dataset into training and tests. The training sets are used to train the machine learning algorithms and the test sets are used to evaluate their performance.

train = MultiRegressor(random_state=42,                        cores=-1,                        verbose=True) split = train.split(X=features, y=labels, sizeOfTest=0.2, randomState=42, normalize='StandardScaler', columns_to_scale=num_col, shuffle=True)

The normalize parameter in the split method allows you to scale your numerical columns by just passing in any scalers of your choice; the columns_to_scale parameter then receives a list of the columns you’d like to scale instead of having to scale all columns automatically.

After splitting the features and labels into train, test is appended to a variable named split. This variable then holds X_train, X_test, y_train, and y_test; we would need it in the next function below.

fit =, y=labels, splitting=True, split_data=split)

Run this code in your notebook to view the full model list and scores.

Visualize Model Results

For the sake of people who might prefer to view the model performance results in charts rather than a dataframe. There’s also an option available for you to convert the dataframe into charts. All you have to do is to run the code below.,           t_split=True) Conclusion

MultiTrain exists to help data scientists and machine learning engineers make their job easy. It eliminates repetition and the boring process. With just a few lines of code, you can get your model training and testing immediately.

The assessment metrics shown on the dataframe might also differ based on the problem you’re attempting to solve, such as multiclass, binary, classification, regression, imbalanced datasets, or balanced datasets. You have even more freedom to work on these challenges by passing values to parameters in different methods rather than writing extensive lines of code.

After fitting the models, the results generated in the dataframe shouldn’t be your final results. Since MultiTrain aims to determine the best models that work best for your particular use case, you should pick the best models and perform some hyperparameter tuning on them or even feature engineering on your dataset to further boost the performance.

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