Was the Result Significant?
Results are deemed significant if they are found to have occurred by some reason other than chance.
Learning Objectives
Assess the statistical significance of data for a null hypothesis
Key Takeaways
Key Points

 In statistical testing, a result is deemed statistically significant if it is so extreme (without external variables which would influence the correlation results of the test) that such a result would be expected to arise simply by chance only in rare circumstances.

 If a test of significance gives a pvalue lower than or equal to the significance level, the null hypothesis is rejected at that level.

 Different levels of cutoff trade off countervailing effects. Lower levels – such as 0.01 instead of 0.05 – are stricter, and increase confidence in the determination of significance, but run an increased risk of failing to reject a false null hypothesis.
Key Terms

 statistical significance: A measure of how unlikely it is that a result has occurred by chance.

 null hypothesis: A hypothesis set up to be refuted in order to support an alternative hypothesis; presumed true until statistical evidence in the form of a hypothesis test indicates otherwise.
Statistical significance refers to two separate notions: the pvalue (the probability that the observed data would occur by chance in a given single null hypothesis); or the Type I error rate α (false positive rate) of a statistical hypothesis test (the probability of incorrectly rejecting a given null hypothesis in favor of a second alternative hypothesis).
A fixed number, most often 0.05, is referred to as a significance level or level of significance; such a number may be used either in the first sense, as a cutoff mark for pvalues (each pvalue is calculated from the data), or in the second sense as a desired parameter in the test design (α depends only on the test design, and is not calculated from observed data). In this atom, we will focus on the pvalue notion of significance.
What is Statistical Significance?
Statistical significance is a statistical assessment of whether observations reflect a pattern rather than just chance. When used in statistics, the word significant does not mean important or meaningful, as it does in everyday speech; with sufficient data, a statistically significant result may be very small in magnitude.
The fundamental challenge is that any partial picture of a given hypothesis, poll, or question is subject to random error. In statistical testing, a result is deemed statistically significant if it is so extreme (without external variables which would influence the correlation results of the test) that such a result would be expected to arise simply by chance only in rare circumstances. Hence the result provides enough evidence to reject the hypothesis of ‘no effect’.
For example, tossing 3 coins and obtaining 3 heads would not be considered an extreme result. However, tossing 10 coins and finding that all 10 land the same way up would be considered an extreme result: for fair coins, the probability of having the first coin matched by all 9 others is rare. The result may therefore be considered statistically significant evidence that the coins are not fair.
The calculated statistical significance of a result is in principle only valid if the hypothesis was specified before any data were examined. If, instead, the hypothesis was specified after some of the data were examined, and specifically tuned to match the direction in which the early data appeared to point, the calculation would overestimate statistical significance.
Use in Practice
Popular levels of significance are 10% (0.1), 5% (0.05), 1% (0.01), 0.5% (0.005), and 0.1% (0.001). If a test of significance gives a pvalue lower than or equal to the significance level, the null hypothesis is rejected at that level. Such results are informally referred to as ‘statistically significant (at the p = 0.05 level, etc.)’. For example, if someone argues that “there’s only one chance in a thousand this could have happened by coincidence”, a 0.001 level of statistical significance is being stated. The lower the significance level chosen, the stronger the evidence required. The choice of significance level is somewhat arbitrary, but for many applications, a level of 5% is chosen by convention.
Different levels of cutoff trade off countervailing effects. Lower levels – such as 0.01 instead of 0.05 – are stricter, and increase confidence in the determination of significance, but run an increased risk of failing to reject a false null hypothesis. Evaluation of a given pvalue of data requires a degree of judgment, and rather than a strict cutoff, one may instead simply consider lower pvalues as more significant.
Data Snooping: Testing Hypotheses Once You’ve Seen the Data
Testing hypothesis once you’ve seen the data may result in inaccurate conclusions.
Learning Objectives
Explain how to test a hypothesis using data
Key Takeaways
Key Points

 Testing a hypothesis suggested by the data can very easily result in false positives (type I errors ). If one looks long enough and in enough different places, eventually data can be found to support any hypothesis.

 If the hypothesis was specified after some of the data were examined, and specifically tuned to match the direction in which the early data appeared to point, the calculation would overestimate statistical significance.

 Sometimes, people deliberately test hypotheses once they’ve seen the data. Data snooping (also called data fishing or data dredging) is the inappropriate (sometimes deliberately so) use of data mining to uncover misleading relationships in data.
Key Terms

 Type I error: Rejecting the null hypothesis when the null hypothesis is true.

 data snooping: the inappropriate (sometimes deliberately so) use of data mining to uncover misleading relationships in data
The calculated statistical significance of a result is in principle only valid if the hypothesis was specified before any data were examined. If, instead, the hypothesis was specified after some of the data were examined, and specifically tuned to match the direction in which the early data appeared to point, the calculation would overestimate statistical significance.
Testing Hypotheses Suggested by the Data
Testing a hypothesis suggested by the data can very easily result in false positives (type I errors). If one looks long enough and in enough different places, eventually data can be found to support any hypothesis. Unfortunately, these positive data do not by themselves constitute evidence that the hypothesis is correct. The negative test data that were thrown out are just as important, because they give one an idea of how common the positive results are compared to chance. Running an experiment, seeing a pattern in the data, proposing a hypothesis from that pattern, then using the same experimental data as evidence for the new hypothesis is extremely suspect, because data from all other experiments, completed or potential, has essentially been “thrown out” by choosing to look only at the experiments that suggested the new hypothesis in the first place.
A large set of tests as described above greatly inflates the probability of type I error as all but the data most favorable to the hypothesis is discarded. This is a risk, not only in hypothesis testing but in all statistical inference as it is often problematic to accurately describe the process that has been followed in searching and discarding data. In other words, one wants to keep all data (regardless of whether they tend to support or refute the hypothesis) from “good tests”, but it is sometimes difficult to figure out what a “good test” is. It is a particular problem in statistical modelling, where many different models are rejected by trial and error before publishing a result.
The error is particularly prevalent in data mining and machine learning. It also commonly occurs in academic publishing where only reports of positive, rather than negative, results tend to be accepted, resulting in the effect known as publication bias..
Data Snooping
Sometimes, people deliberately test hypotheses once they’ve seen the data. Data snooping (also called data fishing or data dredging) is the inappropriate (sometimes deliberately so) use of data mining to uncover misleading relationships in data. Datasnooping bias is a form of statistical bias that arises from this misuse of statistics. Any relationships found might appear valid within the test set but they would have no statistical significance in the wider population. Although datasnooping bias can occur in any field that uses data mining, it is of particular concern in finance and medical research, which both heavily use data mining.
Was the Result Important?
The results are deemed important if they change the effects of an event.
Learning Objectives
Distinguish the difference between the terms ‘significance’ and ‘importance’ in statistical assessments
Key Takeaways
Key Points

 When used in statistics, the word significant does not mean important or meaningful, as it does in everyday speech; with sufficient data, a statistically significant result may be very small in magnitude.

 Importance is a measure of the effects of the event. A difference can be significant, but not important.

 It is preferable for researchers to not look solely at significance, but to examine effectsize statistics, which describe how large the effect is and the uncertainty around that estimate, so that the practical importance of the effect may be gauged by the reader.
Key Terms

 statistical significance: A measure of how unlikely it is that a result has occurred by chance.

 null hypothesis: A hypothesis set up to be refuted in order to support an alternative hypothesis; presumed true until statistical evidence in the form of a hypothesis test indicates otherwise.
Significance vs. Importance
Statistical significance is a statistical assessment of whether observations reflect a pattern rather than just chance. When used in statistics, the word significant does not mean important or meaningful, as it does in everyday speech; with sufficient data, a statistically significant result may be very small in magnitude.
If a test of significance gives a pvalue lower than or equal to the significance level, the null hypothesis is rejected at that level. Such results are informally referred to as ‘statistically significant (at the p=0.05 level, etc.)’. For example, if someone argues that “there’s only one chance in a thousand this could have happened by coincidence”, a 0.001 level of statistical significance is being stated. Once again, this does not mean that the findings are important.
So what is importance? Importance is a measure of the effects of the event. For example, we could measure two different onecup measuring cups enough times to find that their volumes are statistically different at a significance level of 0.001. But is this difference important? Would this slight difference make a difference in the cookies you’re trying to bake? No. The difference in this case is statistically significant at a certain level, but not important.
Researchers focusing solely on whether individual test results are significant or not may miss important response patterns which individually fall under the threshold set for tests of significance. Therefore along with tests of significance, it is preferable to examine effectsize statistics, which describe how large the effect is and the uncertainty around that estimate, so that the practical importance of the effect may be gauged by the reader.
The Role of the Model
A statistical model is a set of assumptions concerning the generation of the observed data and similar data.
Learning Objectives
Explain the significance of valid models in statistical inference
Key Takeaways
Key Points

 Statisticians distinguish between three levels of modeling assumptions: fully parametric, nonparametric, and semiparametric.

 Descriptions of statistical models usually emphasize the role of population quantities of interest, about which we wish to draw inference. Descriptive statistics are typically used as a preliminary step before more formal inferences are drawn.

 Whatever level of assumption is made, correctly calibrated inference in general requires these assumptions to be correct; i.e., that the data generating mechanisms have been correctly specified.
Key Terms

 Simple Random Sampling: Method where each individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process, and each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals.

 covariate: a variable that is possibly predictive of the outcome under study
Any statistical inference requires assumptions. A statistical model is a set of assumptions concerning the generation of the observed data and similar data. Descriptions of statistical models usually emphasize the role of population quantities of interest, about which we wish to draw inference. Descriptive statistics are typically used as a preliminary step before more formal inferences are drawn.
Degrees of Models
Statisticians distinguish between three levels of modeling assumptions:

 Fullyparametric. The probability distributions describing the datageneration process are assumed to be fully described by a family of probability distributions involving only a finite number of unknown parameters. For example, one may assume that the distribution of population values is truly Normal, with unknown mean and variance, and that data sets are generated by simple random sampling. The family of generalized linear models is a widely used and flexible class of parametric models.

 Nonparametric. The assumptions made about the process generating the data are much fewer than in parametric statistics and may be minimal. For example, every continuous probability distribution has a median that may be estimated using the sample median, which has good properties when the data arise from simple random sampling.

 Semiparametric. This term typically implies assumptions in between fully and nonparametric approaches. For example, one may assume that a population distribution has a finite mean. Furthermore, one may assume that the mean response level in the population depends in a truly linear manner on some covariate (a parametric assumption), but not make any parametric assumption describing the variance around that mean. More generally, semiparametric models can often be separated into ‘structural’ and ‘random variation’ components. One component is treated parametrically and the other nonparametrically.
Importance of Valid Models
Whatever level of assumption is made, correctly calibrated inference in general requires these assumptions to be correct (i.e., that the datagenerating mechanisms have been correctly specified).
Incorrect assumptions of simple random sampling can invalidate statistical inference. More complex semi and fully parametric assumptions are also cause for concern. For example, incorrect “Assumptions of Normality” in the population invalidate some forms of regressionbased inference. The use of any parametric model is viewed skeptically by most experts in sampling human populations. In particular, a normal distribution would be a totally unrealistic and unwise assumption to make if we were dealing with any kind of economic population. Here, the central limit theorem states that the distribution of the sample mean for very large samples is approximately normally distributed, if the distribution is not heavy tailed.
Does the Difference Prove the Point?
Rejecting the null hypothesis does not necessarily prove the alternative hypothesis.
Learning Objectives
Assess whether a null hypothesis should be accepted or rejected
Key Takeaways
Key Points

 The “fail to reject” terminology highlights the fact that the null hypothesis is assumed to be true from the start of the test; therefore, if there is a lack of evidence against it, it simply continues to be assumed true.

 The phrase “accept the null hypothesis” may suggest it has been proven simply because it has not been disproved, a logical fallacy known as the argument from ignorance.

 Unless a test with particularly high power is used, the idea of “accepting” the null hypothesis may be dangerous.

 Whether rejection of the null hypothesis truly justifies acceptance of the alternative hypothesis depends on the structure of the hypotheses.

 Hypothesis testing emphasizes the rejection, which is based on a probability, rather than the acceptance, which requires extra steps of logic.
Key Terms

 pvalue: The probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.

 alternative hypothesis: a rival hypothesis to the null hypothesis, whose likelihoods are compared by a statistical hypothesis test

 null hypothesis: A hypothesis set up to be refuted in order to support an alternative hypothesis; presumed true until statistical evidence in the form of a hypothesis test indicates otherwise.
In statistical hypothesis testing, tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a prespecified level of significance; this can help to decide whether results contain enough information to cast doubt on conventional wisdom, given that conventional wisdom has been used to establish the null hypothesis. The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis.
Accepting the Null Hypothesis vs. Failing to Reject It
It is important to note the philosophical difference between accepting the null hypothesis and simply failing to reject it. The “fail to reject” terminology highlights the fact that the null hypothesis is assumed to be true from the start of the test; if there is a lack of evidence against it, it simply continues to be assumed true. The phrase “accept the null hypothesis” may suggest it has been proved simply because it has not been disproved, a logical fallacy known as the argument from ignorance. Unless a test with particularly high power is used, the idea of “accepting” the null hypothesis may be dangerous. Nonetheless, the terminology is prevalent throughout statistics, where its meaning is well understood.
Alternatively, if the testing procedure forces us to reject the null hypothesis (H0), we can accept the alternative hypothesis ([H1) and we conclude that the research hypothesis is supported by the data. This fact expresses that our procedure is based on probabilistic considerations in the sense we accept that using another set of data could lead us to a different conclusion.
What Does This Mean?
If the pvalue is less than the required significance level (equivalently, if the observed test statistic is in the critical region), then we say the null hypothesis is rejected at the given level of significance. Rejection of the null hypothesis is a conclusion. This is like a “guilty” verdict in a criminal trial—the evidence is sufficient to reject innocence, thus proving guilt. We might accept the alternative hypothesis (and the research hypothesis).
If the pvalue is not less than the required significance level (equivalently, if the observed test statistic is outside the critical region), then the test has no result. The evidence is insufficient to support a conclusion. This is like a jury that fails to reach a verdict. The researcher typically gives extra consideration to those cases where the pvalue is close to the significance level.
Whether rejection of the null hypothesis truly justifies acceptance of the research hypothesis depends on the structure of the hypotheses. Rejecting the hypothesis that a large paw print originated from a bear does not immediately prove the existence of Bigfoot. The two hypotheses in this case are not exhaustive; there are other possibilities. Maybe a moose made the footprints. Hypothesis testing emphasizes the rejection which is based on a probability rather than the acceptance which requires extra steps of logic.
Source: Statistics