Research Aptitude Short Notes (One Liners)

Paper 1 – Short Notes (One Liners)

Short Notes

1. Research:

• Research is a planned study to find new facts or check old facts with proof.
• It uses steps like problem, data, analysis, and conclusion to reduce mistakes.
• It is different from guessing because every answer must be supported by evidence.
  Example: Studying reasons for low marks; Testing a new teaching method; Verifying a survey report.

2. Scientific Method:

• Scientific method is a step-by-step way to find truth through observation and testing.
• It follows problem, hypothesis, experiment, results, and conclusion in a logical order.
• It is different from opinion because others should be able to repeat the same result.
  Example: Testing fertilizer effect; Measuring noise levels; Checking memory improvement after practice.

3. Research Problem:

• A research problem is a clear question that the researcher wants to answer using study.
• It must be specific, researchable, and meaningful, so the work stays focused.
• It is different from a topic because a topic is broad, but a problem is narrow.
  Example: Why students fear math; Does online learning improve scores; What affects reading habits.

4. Statement of Problem:

• Statement of problem is a short, clear description of the issue that needs study.
• It shows the gap and need, so readers understand why the research matters.
• It is different from objectives because it states the issue, not the goals.
  Example: Low attendance in class; Poor reading habit in students; High dropout in a course.

5. Research Objectives:

• Objectives are specific goals that tell what the study will do and achieve.
• They guide tools, sample, and analysis, so the research does not go off-track.
• They are different from aims because objectives are measurable and more specific.
  Example: Compare two methods; Find causes of dropout; Measure effect of counselling.

6. Research Questions:

• Research questions are simple questions the study answers using data and analysis.
• They help choose method and tool, so the researcher collects the right information.
• They differ from hypothesis because questions ask, while hypothesis predicts results.
  Example: What causes absenteeism; How stress affects study; Which method improves retention.

7. Literature Review:

• Literature review is reading and summarizing previous studies on the same topic.
• It helps find gaps, avoid repeating work, and build a strong base for research.
• It is different from a book summary because it focuses on research findings and gaps.
  Example: Reading journals on motivation; Comparing past surveys; Listing findings of old studies.

8. Research Gap:

• Research gap is the missing area or unanswered question in existing studies.
• Finding the gap helps select a useful problem and gives direction for new work.
• It differs from a general interest because a gap is based on evidence from literature.
  Example: Few studies on rural learners; No data for new policy; Missing comparison of methods.

9. Conceptual Framework:

• Conceptual framework is a simple model showing key concepts and their link.
• It helps plan variables, tools, and analysis by showing what relates to what.
• It differs from theory because it may use ideas from many sources, not one theory.
  Example: Study time → Marks; Stress → Performance; Teaching method → Learning outcome.

10. Theoretical Framework:

• Theoretical framework is the theory base used to explain the research problem.
• It helps predict relationships and gives a strong reason for selecting variables.
• It differs from conceptual framework because it is built mainly from one theory.
  Example: Skinner theory for reinforcement; Piaget for development; Maslow for motivation.

11. Hypothesis:

• Hypothesis is a testable prediction about relationship between two or more variables.
• It gives direction and helps decide what data to collect and what test to use.
• It differs from an assumption because it must be checked using evidence and data.
  Example: Practice improves scores; Sleep improves memory; Training reduces errors.

12. Null Hypothesis (H0):

• H0 says there is no significant difference or relationship between variables.
• Researchers test H0 and reject it only when evidence is strong enough.
• It differs from H1 because H0 always states “no effect” or “no difference.”
  Example: No difference between groups; No link between hours and marks; No effect of training.

13. Alternative Hypothesis (H1):

• H1 says there is a significant difference or relationship between variables.
• It is supported when results are strong enough to reject the null hypothesis.
• It differs from H0 because it claims an effect exists, not “no effect.”
  Example: New method improves scores; Exercise improves attention; Sleep increases memory.

14. Variables:

• Variables are factors that can change and can be measured in a study.
• Research checks how one variable influences another using data and analysis.
• Variables differ from constants because constants stay same for everyone in the study.
  Example: Age; Study time; Test marks.

15. Independent Variable:

• Independent variable is the factor the researcher changes or controls as the cause.
• It is the input that may influence the outcome in an experiment or study.
• It differs from dependent variable because dependent variable is measured as result.
  Example: Teaching method; Practice time; Type of training.

16. Dependent Variable:

• Dependent variable is the outcome measured to see the effect of the change.
• It changes because of the independent variable and shows the study result.
• It differs from independent variable because it is the effect, not the cause.
  Example: Test score; Speed of learning; Number of errors.

17. Extraneous Variable:

• Extraneous variables are unwanted factors that can affect results without notice.
• They reduce fairness, so researchers control them or keep conditions similar.
• They differ from independent variable because they are not planned or controlled causes.
  Example: Noise in class; Prior knowledge level; Different teacher behavior.

18. Control Variable:

• Control variables are kept the same so they do not influence the outcome.
• Controlling them makes group comparison fair and improves study accuracy.
• They differ from independent variables because they are not changed during the study.
  Example: Same syllabus; Same time limit; Same classroom environment.

19. Operational Definition:

• Operational definition explains how a variable will be measured in real terms.
• It makes the study clear and repeatable, so others can test the same way.
• It differs from general meaning because it gives the exact measurement method.
  Example: Stress as scale score; Achievement as exam marks; Attendance as days present.

20. Population:

• Population is the total group about which the researcher wants to conclude.
• It may be very large, so studying everyone is often difficult and costly.
• It differs from sample because sample is only a small selected part of population.
  Example: All UGC NET aspirants; All teachers in a district; All students in a university.

21. Sample:

• Sample is a smaller group chosen from the population for actual study.
• A good sample represents the population, so findings become more reliable.
• It differs from population because it is manageable and used for practical reasons.
  Example: 200 college students; 50 school teachers; 300 survey respondents.

22. Sampling:

• Sampling is the process of selecting a sample from the population.
• Good sampling saves time and money while still giving useful conclusions.
• It differs from census because census collects data from everyone in population.
  Example: Randomly choosing 100 students; Selecting 10 schools; Picking 500 people for survey.

23. Sampling Frame:

• Sampling frame is the list of all population members from which sample is chosen.
• A correct frame improves selection accuracy and reduces missing or extra members.
• It differs from population because it is a usable list, not the full real group itself.
  Example: Student roll list; Voter list; Employee register list.

24. Census:

• Census means collecting data from every member of the population.
• It gives complete information, but needs more time, cost, and manpower.
• It differs from sampling because sampling studies only a part, not everyone.
  Example: Surveying all households; Counting all students in a small college; Checking all staff records.

25. Probability Sampling:

• Probability sampling gives every member a known chance to be selected.
• It reduces bias and supports better generalization from sample to population.
• It differs from non-probability sampling where selection chance is not known.
  Example: Simple random sampling; Stratified sampling; Cluster sampling.

26. Non-Probability Sampling:

• Non-probability sampling does not give equal or known chance to all members.
• It is easy and fast, but it may produce biased results and weak generalization.
• It differs from probability sampling because selection depends on choice or availability.
  Example: Convenience sampling; Purposive sampling; Snowball sampling.

27. Simple Random Sampling:

• Simple random sampling gives each member an equal chance of selection.
• It reduces selection bias when the population list is available and correct.
• It differs from systematic sampling because selection is by pure random, not fixed interval.
  Example: Lottery method; Random number table; Computer random selection.

28. Systematic Sampling:

• Systematic sampling selects every kth person after choosing a random start.
• It is simple and quick when a list is available and ordered properly.
• It differs from random sampling because selection follows a fixed interval pattern.
  Example: Every 10th student; Every 5th house; Every 20th entry in a register.

29. Stratified Sampling:

• Stratified sampling divides population into strata and takes sample from each stratum.
• It gives better representation when groups are different, like gender or region.
• It differs from cluster sampling because strata are made to be similar within each group.
  Example: Sample from each class; Sample from each district; Sample from male and female groups.

30. Cluster Sampling:

• Cluster sampling divides population into clusters and selects some clusters for study.
• It saves cost when population is spread widely and listing all members is hard.
• It differs from stratified sampling because clusters are mini populations, not similar groups.
  Example: Selecting 10 schools as clusters; Selecting villages as clusters; Selecting city blocks as clusters.

31. Convenience Sampling:

• Convenience sampling selects people who are easiest to reach for the researcher.
• It is fast and cheap, but results may be biased and not represent population well.
• It differs from random sampling because selection is based on ease, not equal chance.
  Example: Students in one class; People near a shop; Friends and neighbors for survey.

32. Purposive Sampling:

• Purposive sampling selects participants who fit a specific purpose or quality.
• It is useful in qualitative studies where special cases are needed for deep study.
• It differs from convenience sampling because selection is based on need, not only ease.
  Example: Selecting expert teachers; Choosing toppers for interview; Selecting special needs learners.

33. Snowball Sampling:

• Snowball sampling uses existing participants to refer new participants to the study.
• It helps reach hidden or hard-to-find groups where lists are not available.
• It differs from random sampling because selection grows through contacts, not equal chance.
  Example: Finding drug users through referrals; Locating rare disease patients; Reaching niche professional groups.

34. Sample Size:

• Sample size is the number of participants selected for a study.
• Too small sample gives weak results, too large sample increases cost and time.
• It differs from population size because sample is chosen number, not total members.
  Example: Sample of 100 students; Sample of 500 households; Sample of 60 teachers.

35. Data:

• Data are facts, numbers, or observations collected for answering research questions.
• Data can be quantitative (numbers) or qualitative (words) based on study needs.
• Data differ from opinion because data are collected systematically using tools and rules.
  Example: Test scores; Interview responses; Attendance records.

36. Primary Data:

• Primary data are collected first-hand by the researcher for the current study.
• They are original and specific, but need more time, cost, and effort to collect.
• They differ from secondary data because they are not taken from existing sources.
  Example: Conducting a survey; Taking interviews; Recording observations.

37. Secondary Data:

• Secondary data are already collected by someone else and used again in new study.
• They save time and cost, but may not fit the present study perfectly.
• They differ from primary data because researcher did not collect them first-hand.
  Example: Using census report; Using journal articles; Using school records.

38. Data Collection Methods:

• Data collection methods are ways to gather information for the research study.
• The right method depends on objectives, sample, and type of data needed.
• Methods differ from tools; method is approach, tool is the actual form or instrument.
  Example: Questionnaire method; Interview method; Observation method.

39. Questionnaire:

• Questionnaire is a set of written questions used to collect information from many people.
• It is useful for large samples and gives quick data when questions are clear.
• It differs from interview because there is less personal interaction and less probing.
  Example: Google form survey; Printed feedback form; Multiple-choice questionnaire.

40. Interview:

• Interview is direct questioning to collect detailed information from participants.
• It allows probing and clarifying doubts, so answers can be deeper and clearer.
• It differs from questionnaire because it needs time and skill, but gives rich data.
  Example: Face-to-face interview; Phone interview; Structured interview schedule.

41. Observation:

• Observation is collecting data by watching behavior, events, or situations carefully.
• It is useful when people may not report truthfully, but actions show reality.
• It differs from interview because it records what people do, not only what they say.
  Example: Classroom observation; Lab behavior observation; Traffic observation study.

42. Experiment:

• Experiment tests cause-and-effect by changing independent variable and measuring outcome.
• It uses control and experimental groups to make a fair and clear comparison.
• It differs from survey because survey mainly describes, while experiment tests effect.
  Example: Testing new teaching method; Testing memory tool; Testing training impact.

43. Survey Research:

• Survey research collects data from many people to describe opinions or behaviors.
• It is useful for general trends and large samples using questionnaires or interviews.
• It differs from case study because survey is broad, while case study is deep and narrow.
  Example: Student satisfaction survey; Teacher opinion survey; Community awareness survey.

44. Case Study:

• Case study is an in-depth study of one person, group, school, or event.
• It gives detailed understanding, but results may not generalize to all people.
• It differs from survey because it focuses deeply on one case, not many cases.
  Example: Studying one school’s success; Studying one dropout student; Studying one village program.

45. Historical Research:

• Historical research studies past events using old records, documents, and reports.
• It helps understand causes and effects over time and learn lessons from history.
• It differs from experimental research because it cannot control variables from the past.
  Example: Studying old education policies; Analyzing past reforms; Using archives and reports.

46. Descriptive Research:

• Descriptive research describes what exists, like behaviors, opinions, or conditions.
• It answers “what is happening” without changing variables or testing cause strongly.
• It differs from experimental research because it observes and reports, not manipulates variables.
  Example: Describing reading habits; Reporting attendance patterns; Studying study-time patterns.

47. Analytical Research:

• Analytical research uses existing data to analyze and explain patterns or relationships.
• It goes beyond description and tries to interpret why something is happening.
• It differs from descriptive research because it focuses more on analysis and reasoning.
  Example: Analyzing exam data; Studying trend reports; Explaining pattern from records.

48. Exploratory Research:

• Exploratory research is done when little information is available about a problem.
• It helps form ideas, variables, and questions for deeper future research.
• It differs from conclusive research because it is flexible and open-ended.
  Example: Exploring reasons for low interest; Pilot interviews; Small focus group discussions.

49. Explanatory Research:

• Explanatory research tries to explain why and how variables are related.
• It often uses hypothesis testing and stronger design to understand cause-like links.
• It differs from exploratory research because it starts with clearer questions and plans.
  Example: Explaining stress and marks link; Explaining teaching method impact; Explaining dropout reasons.

50. Experimental Research:

• Experimental research tests cause-and-effect by controlling conditions and variables.
• It uses control group and experimental group to compare outcomes fairly.
• It differs from correlational research because correlation does not prove cause.
  Example: Comparing two teaching methods; Testing memory training; Testing new learning app.

51. Quasi-Experimental Research:

• Quasi-experimental research tests effect, but groups may not be randomly formed.
• It is used in schools where random assignment is hard, but comparison still happens.
• It differs from true experiment because randomization and full control are limited.
  Example: Comparing two existing classes; Testing program in one school; Comparing before-after results.

52. Correlational Research:

• Correlational research studies relationship between variables without changing them.
• It tells direction and strength of relationship, but it does not prove cause-effect.
• It differs from experimental research because no variable is controlled or manipulated.
  Example: Study time and marks; Sleep hours and attention; Stress score and performance.

53. Longitudinal Study:

• Longitudinal study collects data from same group over a long period of time.
• It helps see changes and development patterns clearly across months or years.
• It differs from cross-sectional study which collects data only at one time.
  Example: Tracking learners for 2 years; Following same teachers yearly; Monitoring growth over semesters.

54. Cross-Sectional Study:

• Cross-sectional study collects data at one point in time from different people.
• It is quick and useful to compare groups, but it cannot show changes over time.
• It differs from longitudinal study which follows the same group for a long time.
  Example: One-time student survey; Comparing age groups once; One-time college feedback.

55. Qualitative Research:

• Qualitative research focuses on meanings, experiences, and descriptions using words.
• It uses interviews, observation, and case studies for deep understanding.
• It differs from quantitative research because it is not mainly about numbers.
  Example: Student experience interviews; Classroom observation notes; Case study reports.

56. Quantitative Research:

• Quantitative research uses numbers, measurement, and statistical analysis for conclusions.
• It often uses tests and surveys to compare groups or test relationships.
• It differs from qualitative research because it needs measurable variables and numeric data.
  Example: Test score analysis; Survey percentage results; Comparing mean marks of groups.

57. Mixed Methods Research:

• Mixed methods research uses both qualitative and quantitative methods in one study.
• It gives both depth (why) and measurement (how much) for a stronger conclusion.
• It differs from single method because it combines strengths of both approaches.
  Example: Survey plus interviews; Test scores plus observation; Statistics plus case studies.

58. Measurement Scales:

• Measurement scales are ways to record data as categories, ranks, or numbers.
• Choosing correct scale helps select correct statistical test and correct interpretation.
• It differs across types, because nominal is label, while ratio has true zero.
  Example: Gender category; Rank order list; Marks out of 100.

59. Nominal Scale:

• Nominal scale classifies data into names or categories without any order.
• It is used for counting groups, and numbers are labels, not real values.
• It differs from ordinal scale because nominal has no ranking or level order.
  Example: Male or Female; Urban or Rural; Subject stream names.

60. Ordinal Scale:

• Ordinal scale gives ranking or order, but gaps between ranks are not equal.
• It tells higher or lower, but it cannot tell exact difference between two ranks.
• It differs from interval scale because interval has equal gaps between values.
  Example: 1st 2nd 3rd rank; Likert levels; Grade A B C.

61. Interval Scale:

• Interval scale has equal gaps between values, but it has no true zero point.
• You can add and subtract meaningfully, but ratio statements like “twice” are not correct.
• It differs from ratio scale because ratio has true zero and supports multiplication.
  Example: Temperature in Celsius; IQ score; Calendar years.

62. Ratio Scale:

• Ratio scale has equal gaps and also has a true zero, so ratios make sense.
• It supports all operations like add, subtract, multiply, and divide meaningfully.
• It differs from interval because “0” really means none on ratio scale.
  Example: Height in cm; Weight in kg; Time taken in seconds.

63. Validity:

• Validity means a test or tool measures what it is supposed to measure.
• High validity gives correct conclusions and supports correct decisions from results.
• It differs from reliability because a tool can be consistent but still measure wrong thing.
  Example: Aptitude test for aptitude; Math test for math skill; Anxiety scale for anxiety.

64. Reliability:

• Reliability means a tool gives consistent results when repeated in similar conditions.
• It reduces random errors and increases trust in scores or measurements.
• It differs from validity because reliability is consistency, not correctness of what is measured.
  Example: Same test gives similar scores; Stable questionnaire results; Consistent rating by same rater.

65. Pilot Study:

• Pilot study is a small trial run done before the main research starts.
• It helps check tool clarity, time needed, and possible problems in data collection.
• It differs from main study because it is small and mainly for improvement and testing plan.
  Example: Testing questionnaire on 20 people; Trial interview; Small pre-test survey.

66. Sampling Error:

• Sampling error is the difference between sample result and true population result.
• It happens due to chance selection, and it reduces when sample size increases.
• It differs from bias because bias is systematic, while sampling error is random.
  Example: Sample mean differs from true mean; Sample percent differs from actual percent; Different sample gives different result.

67. Bias:

• Bias is an unfair influence that pushes results in one direction systematically.
• It can come from poor sampling, leading questions, or researcher expectations.
• It differs from sampling error because bias does not reduce easily even with larger sample.
  Example: Leading questions in form; Only selecting toppers; Researcher favoritism in observation.

68. Ethics in Research:

• Research ethics are rules to protect participants and keep research honest.
• Ethics include consent, safety, privacy, truthful reporting, and avoiding harm.
• It differs from skill because ethics is about right behavior, not only correct method.
  Example: Taking permission; Protecting identity; Reporting true findings.

69. Informed Consent:

• Informed consent means participants agree after knowing purpose, process, and risks.
• It ensures participation is voluntary and people can withdraw anytime without fear.
• It differs from forced participation because consent must be free and clear.
  Example: Signed consent form; Consent before interview; Consent before recording audio.

70. Confidentiality:

• Confidentiality means participant information is kept private and not shared openly.
• It builds trust and protects participants from harm or embarrassment.
• It differs from anonymity because confidentiality can still keep identity known to researcher.
  Example: Storing data safely; Using code numbers; Not sharing names in report.

71. Anonymity:

• Anonymity means even the researcher cannot link responses to a person’s identity.
• It protects participants strongly and reduces fear in sensitive topics.
• It differs from confidentiality because confidentiality may keep identity known but hidden.
  Example: Anonymous online form; No name on questionnaire; Using random ID without linking list.

72. Plagiarism:

• Plagiarism means using someone else’s words, ideas, or work without giving credit.
• It is unethical and can cause rejection of thesis, paper, or project work.
• It differs from paraphrasing with citation because citation gives proper credit to source.
  Example: Copying from a paper; Copying from a website; Using others’ data without source.

73. Citation and Referencing:

• Citation means mentioning source inside text, and referencing means listing sources at end.
• It shows honesty and helps readers verify information and read more details.
• It differs from plagiarism because proper citation gives credit and avoids stealing work.
  Example: APA style citation; MLA style reference list; Adding DOI or journal details.

74. Research Design:

• Research design is the full plan of how the study will be done from start to end.
• It includes sample, tools, procedure, and analysis method to answer the research questions.
• It differs from method because design is the blueprint, while method is one part of it.
  Example: Experimental design plan; Survey design plan; Case study design plan.

75. Data Analysis:

• Data analysis means organizing, cleaning, and interpreting data to find meaning.
• It helps answer research questions using tables, graphs, coding, or statistics.
• It differs from data collection because analysis starts after data is gathered.
  Example: Calculating averages; Coding interview themes; Creating charts from survey.

76. Descriptive Statistics:

• Descriptive statistics summarize data in simple form for easy understanding.
• It includes mean, median, mode, percentage, and graphs to describe patterns.
• It differs from inferential statistics because it describes sample data, not generalizes to population.
  Example: Finding average marks; Showing pie chart; Calculating percentage of pass students.

77. Inferential Statistics:

• Inferential statistics help draw conclusions about population using sample data.
• It uses tests like t-test, ANOVA, chi-square, and p-value to decide significance.
• It differs from descriptive statistics because it goes beyond description to decision making.
  Example: Testing group difference; Testing association; Generalizing from sample to population.

78. t-test:

• t-test checks whether the mean difference between two groups is significant.
• It is commonly used when comparing two sets like control group and experimental group.
• It differs from ANOVA because ANOVA is used for three or more group means.
  Example: Comparing boys and girls marks; Comparing two teaching methods; Comparing pre-test and post-test.

79. ANOVA:

• ANOVA checks whether mean differences among three or more groups are significant.
• It helps compare multiple methods or multiple groups without doing many t-tests.
• It differs from t-test because it handles more than two group means at one time.
  Example: Comparing three teaching methods; Comparing three colleges’ performance; Comparing three training plans.

80. Chi-square Test:

• Chi-square test checks association between categorical variables using frequency data.
• It is used when data are in categories, not continuous numbers like marks.
• It differs from t-test because t-test compares means, while chi-square compares frequencies.
  Example: Gender and choice of stream; Pass or fail by method; Urban or rural by awareness.

50 Most Asked in PYQs One Liners

1. Hypothesis is a testable prediction about variables.
2. Null Hypothesis states “no difference” or “no relationship.”
3. Alternative Hypothesis states a difference or relationship exists.
4. Independent Variable is the cause/input controlled by the researcher.
5. Dependent Variable is the outcome/result measured in the study.
6. Population is the complete group targeted for conclusions.
7. Sample is a smaller group selected from the population.
8. Sampling is the process of selecting participants from population.
9. Census studies every member of the population.
10. Probability Sampling gives known chance to each member.
11. Non-Probability Sampling does not give known chance to each member.
12. Random Sampling gives equal chance to all members.
13. Stratified Sampling takes sample from each stratum/group.
14. Cluster Sampling selects some clusters and studies members inside them.
15. Convenience Sampling selects participants based on easy availability.
16. Purposive Sampling selects participants based on specific purpose.
17. Snowball Sampling uses participant referrals to find more participants.
18. Primary Data is collected first-hand for the current study.
19. Secondary Data is taken from existing sources like reports and journals.
20. Questionnaire is a written tool to collect responses.
21. Interview collects data by direct questioning.
22. Observation collects data by watching behavior or events.
23. Experiment tests cause-and-effect by controlling variables.
24. Survey studies trends from many people at one time.
25. Case Study deeply studies one case/person/group.
26. Qualitative Research uses words and meanings for deep understanding.
27. Quantitative Research uses numbers and statistics for conclusions.
28. Mixed Methods combines qualitative and quantitative approaches.
29. Nominal Scale is category data with no order.
30. Ordinal Scale is rank data with order but unequal gaps.
31. Interval Scale has equal gaps but no true zero.
32. Ratio Scale has equal gaps and true zero.
33. Validity means measuring what should be measured.
34. Reliability means giving consistent results.
35. Pilot Study is a small trial before main research.
36. Bias is systematic unfair influence on results.
37. Sampling Error is chance difference between sample and population results.
38. Ethics demands consent, safety, and honest reporting.
39. Informed Consent means voluntary agreement after knowing details.
40. Confidentiality means keeping participant details private.
41. Anonymity means identity cannot be linked to responses.
42. Plagiarism is using others’ work without giving credit.
43. Citation means mentioning source inside the text.
44. Referencing means listing sources at the end.
45. Research Design is the blueprint of the whole research plan.
46. Descriptive Statistics summarize data using mean, median, mode, charts.
47. Inferential Statistics generalize from sample to population using tests.
48. t-test compares mean difference between two groups.
49. ANOVA compares mean difference among three or more groups.
50. Chi-square tests association between categorical variables.

70 Confusing Pairs / Differences

1. Research vs Survey — Research is the full study process; survey is one data collection method.
2. Population vs Sample — Population is total group; sample is a part of it.
3. Census vs Sampling — Census studies all members; sampling studies selected members.
4. Primary Data vs Secondary Data — Primary is first-hand; secondary is already existing.
5. Qualitative vs Quantitative — Qualitative uses meanings/words; quantitative uses numbers/statistics.
6. Hypothesis vs Assumption — Hypothesis is testable; assumption may not be tested.
7. Null Hypothesis vs Alternative Hypothesis — H0 says no effect; H1 says effect exists.
8. Independent Variable vs Dependent Variable — Independent is cause/input; dependent is outcome/result.
9. Extraneous Variable vs Control Variable — Extraneous disturbs results; control is kept constant to protect results.
10. Reliability vs Validity — Reliability is consistency; validity is correctness of measurement.
11. Correlation vs Causation — Correlation shows relation; causation shows cause-effect.
12. Questionnaire vs Interview — Questionnaire is written; interview is direct questioning.
13. Observation vs Experiment — Observation watches naturally; experiment controls variables to test effect.
14. Descriptive Research vs Experimental Research — Descriptive describes; experimental tests cause-effect.
15. Case Study vs Survey — Case study is deep one case; survey is broad many people.
16. Longitudinal vs Cross-sectional — Longitudinal follows same group over time; cross-sectional is one-time study.
17. Probability Sampling vs Non-Probability Sampling — Probability has known chance; non-probability has unknown chance.
18. Random Sampling vs Convenience Sampling — Random is chance-based; convenience is ease-based.
19. Stratified Sampling vs Cluster Sampling — Stratified samples from each group; cluster selects some groups as units.
20. Nominal Scale vs Ordinal Scale — Nominal is category only; ordinal is category with order.
21. Ordinal Scale vs Interval Scale — Ordinal has unequal gaps; interval has equal gaps but no true zero.
22. Interval Scale vs Ratio Scale — Interval has no true zero; ratio has true zero.
23. Confidentiality vs Anonymity — Confidentiality hides identity; anonymity cannot link identity at all.
24. Pilot Study vs Main Study — Pilot is small trial; main study is full research.
25. Bias vs Sampling Error — Bias is systematic; sampling error is random chance difference.
26. Validity vs Face Validity — Validity is actual correctness; face validity is only “looks correct” on surface.
27. t-test vs ANOVA — t-test compares two means; ANOVA compares three or more means.
28. t-test vs Chi-square — t-test compares means; chi-square compares category frequencies/association.
29. Data Collection vs Data Analysis — Collection gathers information; analysis interprets and finds meaning.
30. Citation vs Referencing — Citation is inside text; referencing is full source list at the end.
31. Abstract vs Summary — Abstract is a short research overview with purpose/method/result; summary is a general short form of any content.
32. Abstract vs Introduction — Abstract is the whole study in brief; introduction sets background and problem.
33. Introduction vs Literature Review — Introduction gives context and problem; literature review covers past studies and gaps.
34. Aim vs Objective — Aim is broad intention; objective is specific and measurable goal.
35. Objective vs Research Question — Objective states what to do; research question asks what to find.
36. Research Question vs Hypothesis — Question asks; hypothesis predicts a testable answer.
37. Conceptual Framework vs Theoretical Framework — Conceptual is a concept-link model; theoretical is based mainly on a theory.
38. Theory vs Model — Theory explains why; model shows how parts are connected in a simple form.
39. Methodology vs Methods — Methodology is overall approach/logic; methods are specific tools and steps.
40. Research Design vs Research Method — Design is the full blueprint; method is one technique used in it.
41. Tool vs Technique — Tool is the instrument (form/test); technique is how you use it (procedure).
42. Schedule vs Questionnaire — Schedule is filled by investigator; questionnaire is filled by respondent.
43. Structured Interview vs Unstructured Interview — Structured uses fixed questions; unstructured is flexible and open.
44. Open-ended vs Close-ended — Open-ended allows free answers; close-ended gives fixed options.
45. Interview vs Observation — Interview collects what people say; observation records what people do.
46. Participant Observation vs Non-participant Observation — Participant joins the group; non-participant only watches.
47. Field Study vs Laboratory Study — Field is real setting; lab is controlled setting.
48. Experiment vs Quasi-experiment — Experiment uses random assignment; quasi-experiment usually does not.
49. Pre-test vs Post-test — Pre-test is before treatment; post-test is after treatment.
50. Control Group vs Experimental Group — Control gets no new treatment; experimental gets the treatment.
51. Internal Validity vs External Validity — Internal shows true cause-effect inside the study; external shows results can apply to other people/places.
52. Validity vs Reliability — Validity is measuring the right thing; reliability is measuring consistently.
53. Content Validity vs Construct Validity — Content checks full topic coverage; construct checks if tool truly measures the concept.
54. Face Validity vs Content Validity — Face “looks correct” on surface; content covers the full syllabus area properly.
55. Primary Source vs Secondary Source — Primary is original record/data; secondary is explanation or review of originals.
56. Published Data vs Unpublished Data — Published is publicly available; unpublished is private records like files, diaries, reports.
57. Qualitative Data vs Quantitative Data — Qualitative is words/meaning; quantitative is numbers/measurement.
58. Continuous Data vs Discrete Data — Continuous can take any value in a range; discrete has countable whole values.
59. Parameter vs Statistic — Parameter describes population; statistic describes sample.
60. Population Mean (μ) vs Sample Mean (x̄) — μ is true average of population; x̄ is average of sample.
61. Census vs Sample Survey — Census collects from all; sample survey collects from selected few.
62. Sampling Error vs Non-sampling Error — Sampling error comes from sample choice; non-sampling error comes from tool, bias, non-response, recording mistakes.
63. Random Error vs Systematic Error — Random error varies by chance; systematic error repeats in one direction (bias).
64. Bias vs Error — Bias is consistent unfair shift; error is any mistake that reduces accuracy.
65. Response Bias vs Non-response Bias — Response bias is wrong answers; non-response bias is missing answers from some groups.
66. Leading Question vs Neutral Question — Leading pushes one answer; neutral allows free and fair response.
67. Likert Scale vs Rating Scale — Likert uses agree–disagree statements; rating scale gives score level (1–5, poor–excellent).
68. Nominal Scale vs Ordinal Scale — Nominal is category only; ordinal is category with order.
69. Interval Scale vs Ratio Scale — Interval has no true zero; ratio has true zero so “twice” makes sense.
70. Mean vs Median — Mean is average; median is middle value after ordering data.