This will be useful to social researchers and research philosophers. The purpose of the tutorial is to encourage young researchers to reflect on the philosophical dimension of research and motivate them to question whether they must follow a straight-jacketed research method and the null hypothesis statistical testing (NHST) technique. The philosophical dimension as used here refers to a questioning mind and not to the philosophical underpinnings of research (namely, ontology, epistemology, methodology, method, and axiology).

The article focuses on the hypothesis statement in the context of social scientific research and asks some fundamental questions that bear on the confusion and complications in stating a hypothesis and subsequent hypothesis testing. The questions are fundamental as formulating, and testing hypotheses constitute the key “distinguishing characteristic of the scientific method” (1), (p. 22). In short, the questions are as follows: (1) What is a hypothesis? What are the different kinds of hypotheses? (2) What is an acceptable format for a research hypothesis? (3) Is the historically used mix of the classical Fisher’s approach (F-test) and the more structured Neyman and Pearson’s approach (suggesting significance level or Type I error, α; Type II error; β; and statistical power, 1-β) for NHST appropriate? [Refer to Cumming (2) for meaningful comprehensive exposure to the controversy and the mix]. Are there alternatives to the conventional approaches to hypothesis testing and interpretation that provide a more precise conclusion and help in handling the controversy?

The article organizes the contents into six sections, including this section on the introduction. The next three sections present the confusion and complication to elaborate on the questions raised in the above paragraph. The fifth section presents a social research framework, which integrates the discussion on the hypothesis statement and statistical testing. The final section concludes the discussion.

Most students writing master’s dissertation, many research scholars, and researchers state a null hypothesis upfront in their report. They are generally confused or do not have a clear answer to the question, “Should I state the null hypothesis or the alternative hypothesis at the beginning of my report?” Unfortunately, since they do not get a clear answer to the question, they continue with what they think is appropriate. Non-clarity on the distinction between the research hypothesis and statistical hypotheses complicates the issue further. This section presents different kinds of hypotheses and their statement to salvage the confusion.

What is a hypothesis? Are there different kinds of hypotheses? Researchers and scholars have conceptualized hypotheses in different ways. A hypothesis is: (1) a tentative proposition that suggests a solution to a problem or as an explanation of some phenomenon; a hypothesis relates theory to observation and observation to theory (3); (2) a conjectural statement of the relation between two or more variables; it is a relational proposition (4); (3) a formal statement of the expected relationship between an independent and dependent variable (5). The hypothesis is a tentative explanation that accounts for a set of effects and can be tested by further investigation.

As a prediction, a hypothesis is an educated guess as to how a scientific experiment will turn out. It is an educated guess because it is based on previous research, training, observation, and a review of the relevant research literature. Two basic definitions of a hypothesis form the root of the discussion on the different kinds of hypothesis statements below: (1) a hypothesis is a tentative answer to a research question; and (2) a hypothesis is a predictive statement related to the research question. This subsection focuses on the different kinds of hypotheses.

A review of statistical inference comprehensively summarizes different kinds of hypotheses, their statement, and testing (1), (pp. 22–33). The key concept that the summary includes are as follows: (1) direct and indirect statements. Direct statements of hypotheses are inferred from directly observable limited phenomena, e.g., “This rat is running.” An indirect statement is a hypothesis inferred through inductive inference, e.g., “All rats run under condition X.” Hypothesis testing relates an indirect statement to a “scientific [research] hypothesis” and a direct statement to “statistical hypotheses,” which relate to different entities:

“A statistical hypothesis is a statement about one or more parameters of population distributions; it refers to a situation that might be true … scientific hypotheses [or research hypotheses] refer to the phenomena of nature and men” (6), cited in Kirk (1), (p. 22).

(2) A research hypothesis is deduced from the existing literature and/or the real-world phenomena through the hypothetico-deductive approach. The truth or falsity of a research hypothesis cannot be directly verified. Probabilistic verification requires stating and testing a statistical hypothesis to infer the research hypothesis. There are two kinds of statistical hypotheses, namely, null hypothesis, and alternate hypothesis. A null hypothesis is a statement of no effect of one variable on the other or no relationship between the variables. The research hypothesis is by default the alternate hypothesis. Statistical verification involves NHST under certain decision criteria. The section titled “The New Statistics for Hypothesis Testing” below summarizes the limitations of NHST and indicates an alternative to test the statistical significance of the hypothesis.

The above summary suggests that a researcher must state the research hypothesis upfront. The need for a statistical hypothesis appears only at the data analysis stage though it can be stated in the section dealing with the research design.

What are the situations/conditions that require a tentative answer or a prediction? Predictions entail future orientation. For example, a researcher who employs some treatment to participants in a study creates an empirical situation to establish which prediction is appropriate. The next section titled “Different Formats of Hypothesis Statement” discusses the hypothesis as a predictive statement.

There is generally a debate and confusion among academicians from different disciplines (social sciences, sciences, and engineering) about the format of the hypothesis statement. Basically, this means how the hypothesis is set out. Primarily, there are two formats under which various other formats can be accommodated: (i) “X is related to Y” and (ii) “X will be related to Y.” In this article, the former setting is referred to as the is format, and the latter as the will format. As discussed below, the is format posits a tentative answer to the research question under investigation. The alternative format predicts the expected relationship between the variables. This section is an attempt at analyzing the above confusion.

American Psychological Association clearly states that NHST is a starting point, and additional measures (confidence intervals, CI; effect size, ES; and extensive description) must be added to convey the most complete meaning of the results (11), (Section “3.7. Quantitative research standards: statistics and data analysis”). Cumming highlights the limitations and drawbacks of NHST with the help of several examples and illustrations and suggests:

“… we should shift emphasis as much as possible from NHST to estimation, based on effect size and confidence intervals. Effect sizes and confidence intervals provide more complete information than does NHST. Meta-analysis allows accumulation of evidence over a number of studies” (2), (p. ix).

The reporting of confidence intervals and effect size entered in APA publications right at the beginning of the twenty-first century: “In 2005, the Journal of Consulting and Clinical Psychology (JCCP) became the first American Psychological Association (APA) journal to require statistical measures of clinical significance, plus effect sizes (ESs) and associated confidence intervals (CIs), for primary outcomes” (12), cited in Odgaard and Fowler (13). The American Psychological Association now requires that the articles submitted to APA journals report CI and ES: “estimates of appropriate effect sizes and confidence intervals must be reported” (14), (p. 33, italics added).

Confidence interval at a given level of confidence (1–α, generally chosen as 95 or 99%) is the range of values that lie between the mean of the estimate of a statistic (M) minus and plus the error in the estimate (M ± μ), where μ is the population mean. CI uses the largest likely estimation error, called the margin of error (MOE = t_critical × SE), where t_critical is the critical value of the test statistic at given degrees of freedom and the level of significance, and SE is the standard error of the test statistic). MOE measures the precision of estimation. Thus, the shorter the CI, the higher the precision. The 95% confidence interval is reported as follows: the 95% CI[M–t_95%(N-1) × s/(N)12, M + t_95%(N-1) × s/(N)12], where N is the sample size, and s is the standard deviation of the sample data distribution. Values in the confidence interval are plausible values of μ. If CI includes the value zero, the null hypothesis cannot be rejected, otherwise, it is rejected. CI also helps in detecting outliers.

Effect size is “any of various measures of the magnitude or meaningfulness of a relationship between two variables” (15), (p. 352). It is a value that indicates the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. One or more measures of effect size constitute “the primary product of a research inquiry” (16). For example, the ES in the analysis of variance is historically reported as a correlation (η²) between the independent and dependent variables; the effect size based on the correlation coefficient is obtained as r². But, there are suggestions to show that the conventional formulae are erroneous, and more accurate alternatives should be used (17). In general, the ES of a correlation coefficient is interpreted as the proportion of variance in one variable explained by the other. It indicates the practical significance (based on the statistical analysis, which is not the same as the economic significance) of the statistic. The statistical significance of a statistic as revealed by the confidence interval should not be confused with the practical significance. Every study must report the practical significance of the findings.

Meta-analysis is a “quantitative technique for synthesizing the results of multiple studies of a phenomenon into a single result by combining the effect size estimates from each study into a single estimate of the combined effect size or into a distribution of effect sizes” (15), (p. 644). It “can produce strong evidence where at first sight there seems to be only weak evidence.” The publication manual requires reporting all variables employed in the study, whether used in the analysis or not and irrespective of their significance level: “even when a characteristic is not used in analysis of the data, reporting it may… prove useful in meta-analytic studies that incorporate the article’s results” (18), p. 30, cited in Cumming (2). Meta-analysis generally turns long CIs into short ones, and thus turns weak evidence into a strong one.

Some researchers make a distinction between hunches, hypotheses, and working hypotheses. “A” “working hypothesis” is little more than the common-sense procedure that people use routinely. Encountering certain facts, certain alternative explanations come to mind and we proceed to test them” (19), cited in Merton (20), (p. 61). “The investigator begins with a hunch or hypothesis, from this he draws various inferences and these, in turn, are subjected to empirical test which confirms or refutes the hypothesis” (20), (p. 176). “During latent learning the rat is building up a “condition” in himself, which I have designated as a set of “hypotheses,” and this condition—these hypotheses—do not then and there show in his behavior” (21), (p. 161).

This section presents a framework of the social research process (SRF, Figure 1) based on the above distinctions that integrate the different kinds and formats of the research hypotheses discussed in the sections above. The framework suggests that social research takes place in the real world, but research operations happen in the conceptual world. Below is a very brief description of the different components of the SRF and their interrelationships relevant to the focus of this article.

The primary objective of scientific research is to understand the real world. Since the real world is complex, the scientist focuses on a part of the real world, which is referred to as the conceptual world in Figure 1. The conceptual world comprises such elements as the extant concepts, constructs, theories, models, and research relevant to the discipline to which the scientist belongs. In an interdisciplinary context, the conceptual world appropriately includes the elements from the interfaces and the domains of the contributing disciplines. Owing to its contents, the conceptual world is primarily representational in nature and is a large storehouse of representational elements indicated above.

For a particular research investigation, the researcher actively creates the research world that encompasses the “doing” or the activity-related elements of research. Activities in the research world are intensely related to the representational elements of the conceptual world. In general, there is a strong interplay among the real world, the conceptual world, and the research world that the social researcher deals with. Broken arrows in Figure 1 indicate inflows to and outflows from a particular world.

The research world can be divided into two subspaces shown as the research operations space and the statistical operations space. Efficient researchers, particularly those involved in quantitative research, develop strong skills to move between these three worlds and the two subspaces.

Figure 1 indicates a distinction between the conceptual and operational hypotheses. Conceptual hypotheses are initial hunches based on observations, facts, and theories.

Thus, conceptual hypotheses indicate research hypotheses. In this sense, they are tentative answers to research questions. Since facts and theories are relatively permanent, conceptual hypotheses are more appropriately stated in the is format. Operational hypotheses derive from conceptual hypotheses and make predictions about the relationships between observed variables. Below is an example to illustrate the difference between these two types of hypotheses.

If a company dealing in soft drinks is in the process of extending one of its brands, the extension may be congruent or incongruent with the existing brand. This leads to the concept of “brand extension incongruity.” What will be the attitude of the consumers toward the extended brand? The concept brand of “purchase intention” reflects the attitude toward the brand. The relationship between these two concepts can be stated as a conceptual hypothesis: “Brand extension incongruity is related to purchase intention.”

The measurement of concepts requires variables at different levels. The company can measure brand extension incongruity at different levels: congruent brand extension (developing another soft drink), moderately incongruent brand extension (developing snacks), and extremely incongruent brand extension (developing apparel). Similarly, purchase intention can be measured as the likelihood that a consumer will purchase the extended brand. A relationship between incongruity and purchase intention is then stated as an operational hypothesis stating the relationship between the variables: “Likelihood of purchasing a congruent brand extension will be more than for the moderately if extremely incongruent brand extensions.” In this form, the operational hypothesis is a predictive statement. In contrast, if some information is available in the literature about the relationship between these two variables (for example a theory or empirical research), the is format is more appropriate: “Likelihood of purchase is related to the brand extension incongruity.” Is there a linear or non-linear relationship between the extension incongruity and purchase intention? The answer to this question depends on the theory that predicts the relationship [refer to (22)].

Social researchers need to distinguish research hypotheses from statistical hypotheses. Research hypotheses directly relate to real-world phenomena, whereas statistical hypotheses relate to population parameters. If no hypothesis testing procedure is required in a particular study, stating statistical hypotheses makes no sense, but stating a research hypothesis is still meaningful.

There are several limitations of the historically used null hypothesis testing approach by psychologists. Most journals (particularly the APA journals) require reporting confidence intervals and effect sizes. These two measures are useful as they reveal the significance of a hypothesis as in the conventional approach and provide additional information. For example, they provide a precision of the measures and practical significance of the findings. The meta-analysis can reveal strong results whereas individual studies may indicate only weak evidence.

The author confirms the following responsibilities: conceptualization of the study and literature survey to get relevant information appropriate to the theme of the manuscript.