With religion, it is like your brain is in a noose, squeezing out any hope of critical thinking about the religious beliefs. Leaving the victim faith-drunk as if all the oxygen of their mental freedom is cut off and hope for self-mastery is but some far away fantasy out of reach.

Atheistic Null Hypothesis: There is no God/Gods
Theists lose socks all the time (events requiring a hypothesis: called an “Alternative Hypothesis” that has to overcome what is called the “Null hypothesis,” which holds that all “Alternative Hypothesis” are untrue in a sense). Therefore, theists who lose socks believe on faith that this loss is perceived as evidence of god acting in the world, which is to offer an “Alternative Hypothesis.” The “Alternative Hypothesis” is claimed to be true because the said believed god claim is suggested to have come to earth to take theists lost socks, and that is the “Alternative Hypothesis,” which theists think proves the truth of their god evidence claim as a result. But is it actually true, a claim of any “Alternative Hypothesis” can and should be tested against the “Null hypothesis” which is now demonstrated:
“Null hypothesis: Missing socks have another answer as there is no evidence to support the belief in the offered “Alternative Hypothesis.”
Thus there is no evidence of god taking socks, so this claim rejected because it is not god evidence.
Atheist point of view belief, is for me at least, that with all the scientific instruments to ain in knowing the world and the universe we all reside we can conclude there is no creator, bo only nature devoid of magic /supernatural anything.
This feet of conclusions is often furthered by the scientific method type thinking, of which there is a presupposition of methodological naturalism. Furthermore, it is prudent, to say nature is devoid of magic /supernatural anything do to the fact that it has weathered all storms of challenge and never, not even in an insignificant way, been shown that anything could justify some claim of something supernatural, that included every god assertion ever offering. Thus the keys to the kingdom as it were, confirm atheism on every lever testable or knowable with a valid and reliable justification…
Therefore, metaphysical naturalism which performs that is no magic no carry dust or timing thinking realizing the god hypothesis has utterly failed and if not for some obvious imprudent thinking error to reach a positive position on the god question. And as such we are completely within epistemic rationality to say when theorizing is would be a presuppositional error as not only do their beliefs lack “Inculpatory Evidence” that shows, or tends to show confirmation of one’s assumptions or arguments.
But instead are rich in the no magic and no god evidence too and thus we can say we have evidence of absence of any god anything and such a complete manner of not finding anything In all the breathtaking amount of things scone has effected traversed over the claims of gods which too often believing evidenceless things and that is loose irresponsible imprudent thinking . Sound think when looking at the evidence there has never been anything supporting a no god Null hypothesis.
Atheistic Null Hypothesis: There is no God/Gods
Alternative hypothesis: There is a God
Results: Insufficient evidence to overturn null hypothesis of no God/Gods.
Here we see them and the stats show atheism is the only choice to not believe the simply unbelievable. Believing in gods or supernatural in nature is to make a presuppositional error by starting a Nothingness only they think they know this evidenceless thing labels gods in a theist’s flawed worldview that makes them wrongly posit a pro-god(s) null hypothesis. 
.Theistic Null Hypothesis: There is a god(s)
.Alternative hypothesis: There is no god(s) (actually the true Atheistic Null Hypothesis: There is no God/Gods)
Results: Insufficient evidence for this claim(s) of god(s) thus (the Null Hypothesis gOD should be thus replaced with a reality warranted belief assertion) though we all know nothing like that happens no, they believe regardless, it’s not like they really ever required evidence to believe thus to them not having disconfirming evidence then their theistic god myth null hypothesis to them” only, was not overturn so they think they are thus Rationalists or something, no, you are thinking at the level of your contrived null hypothesis gOD.
I am trying to break it down but keep its meaning and structure, unpacking all that is in a belief, what is directly connected to it, relatable to it or I am deconstructing things to expose the errors and increase accuracy.

As an axiological atheist, I understand and utilize value or actually “Value Consciousness” to both give a strong moral “axiological” argument (the problem of evil) as well as use it to fortify my humanism and positive ethical persuasion of human helping and care. Value-blindness gives rise to sociopathic evil.

No God: No evidence, No intelligence, and No goodness = Valid Atheism Conclusion

  1. No evidence, to move past the Atheistic Null Hypothesis: There is no God/Gods (in inferential statistics, a Null Hypothesis generally assumed to be true until evidence indicates otherwise. Thus, a Null Hypothesis is a statistical hypothesis that there is no significant difference reached between the claim and the non-claim, as it is relatively provable/demonstratable in reality some way. “The god question” Null Hypothesis is set at as always at the negative standard: Thus, holding that there is no God/Gods, and as god faith is an assumption of the non-evidentiary wishful thinking non-reality of “mystery thing” found in all god talk, until it is demonstratable otherwise to change. Alternative hypothesis: There is a God (offered with no proof: what is a god and how can anyone say they know), therefore, results: Insufficient evidence to overturn the null hypothesis of no God/Gods.
  2. No intelligence, taking into account the reality of the world we do know with 99 Percent Of The Earth’s Species Are Extinct an intelligent design is ridiculous. Five Mass Extinctions Wiped out 99 Percent of Species that have ever existed on earth. Therefore like a child’s report card having an f they need to retake the class thus, profoundly unintelligent design.
  3. No goodness, assessed through ethically challenging the good god assumptions as seen in the reality of pain and other harm of which there are many to demonstrates either a god is not sufficiently good, not real or as I would assert, god if responsible for this world, would make it a moral monster ripe for the problem of evil and suffering (Argument from Evil). God would be responsible for all pain as life could easily be less painful and yet there is mass suffering. In fact, to me, every child born with diseases from birth scream out against a caring or loving god with the power to do otherwise. It could be different as there is Congenital insensitivity to pain (CIP), also known as congenital analgesia, in which a person cannot feel (and has never felt) physical pain.[1]

Disproof by logical contradiction

‘A Logical Impossibility’

(especially in reductio ad absurdum arguments)

In classical logic, a contradiction consists of a logical incompatibility between two or more propositions. It occurs when the propositions, taken together, yield two conclusions which form the logical, usually opposite inversions of each other. Contradiction by the creation of a paradox, Plato’s Euthydemus dialogue demonstrates the need for the notion of contradiction. In the ensuing dialogue, Dionysodorus denies the existence of “contradiction”, all the while that Socrates is contradicting him: “… I in my astonishment said: What do you mean Dionysodorus? I have often heard, and have been amazed to hear, this thesis of yours, which is maintained and employed by the disciples of Protagoras and others before them, and which to me appears to be quite wonderful, and suicidal as well as destructive, and I think that I am most likely to hear the truth about it from you. The dictum is that there is no such thing as a falsehood; a man must either say what is true or say nothing. Is not that your position?” Indeed, Dionysodorus agrees that “there is no such thing as a false opinion … there is no such thing as ignorance” and demands of Socrates to “Refute me.” Socrates responds “But how can I refute you, if, as you say, to tell a falsehood is impossible?”. – Wikipedia

To me, there simply is and rightly must be an intellectual “ethical-belief-responsibility” (burden of proof) to justify the believed truth that is claimed to others is actually demonstrably as being true with valid and reliable reason and/or evidence when it is stated as such. Yes, intellectually one should provide (justificationism) for their assertions that map the sort of governing good habits of belief-formation, belief-maintenance, and belief-relinquishment.

I am an Axiological (Theoretical and Normative VALUE Theorist philosopher) Atheist

Axiology and Value Theory?

“Value theory is a range of approaches to understanding how, why, and to what degree persons value things; whether the object or subject of valuing is a person, idea, object, or anything else. This investigation began in ancient philosophy, where it is called axiology or ethics.”– Wikipedia

“The term “Value Theory” is used in at least three different ways in philosophy. In its broadest sense, “value theory” is a catch-all label used to encompass all branches of moral philosophy, social and political philosophy, aesthetics, and sometimes feminist philosophy and the philosophy of religion — whatever areas of philosophy are deemed to encompass some “evaluative” aspect. In its narrowest sense, “value theory” is used for a relatively narrow area of normative ethical theory particularly, but not exclusively, of concern to consequentialists. In this narrow sense, “value theory” is roughly synonymous with “axiology”. Axiology can be thought of as primarily concerned with classifying what things are good, and how good they are. For instance, a traditional question of axiology concerns whether the objects of value are subjective psychological states or objective states of the world. But in a more useful sense, “value theory” designates the area of moral philosophy that is concerned with theoretical questions about value and goodness of all varieties — the theory of value. The theory of value, so construed, encompasses axiology, but also includes many other questions about the nature of value and its relation to other moral categories. The division of moral theory into the theory of value, as contrasting with other areas of investigation, cross-cuts the traditional classification of moral theory into normative and metaethical inquiry, but is a worthy distinction in its own right; theoretical questions about value constitute a core domain of interest in moral theory, often cross the boundaries between the normative and the metaethical, and have a distinguished history of investigation.” – (Stanford Encyclopedia of Philosophy)

[Antireligionist] My blogs that address Religion: Archaeology, Anthropology, Philosophy and History

In scientific terms, a “LACK OF EVIDENCE” is/can be proof of non-existence. As with all things as new evidence is discovered views can change. I can prove something does not exist by its lack of existence, the box is empty? Is a box empty?

I can prove god is not in the box, but some say I can never prove the box is empty. But is it right to say the god Box is never empty even if we remove its needed contents? It’s an exercise in rhetoric with what we do know to say the god Box is not empty of all evidence and reason thus all it can be is empty of validity. And still today people say empty god boxes are possibly not empty?

The god Box is was and will always “LACK OF EVIDENCE” thus empty proving the god concepts non-existence. Think I am wrong then you go and keep looking or trying to empty that already empty box labeled god devoid of all facts or reason. In my epistemology quest, I generally follow the standard in philosophy JTB: Justified True Beliefs.

Justified True / Beliefs

Justified? To established justification, I use the philosophy called Reliabilism.
“Reliabilism is a general approach to epistemology that emphasizes the truth-conduciveness of a belief-forming process, method, or another epistemologically relevant factor. The reliability theme appears both in theories of knowledge and theories of justification.” Ref

True? For the true part, I use the philosophy called The Correspondence Theory of Truth.
“The correspondence theory of truth states that the truth or falsity of a statement is determined only by how it relates to the world and whether it accurately describes (i.e., corresponds with) that world.” Ref

Beliefs? For the beliefs part, I use what philosophy calls The Ethics of Belief.
“The “ethics of belief” refers the intersection of epistemology, philosophy of mind, psychology, and ethics. The central is norms governing our habits of belief-formation, belief-maintenance, and belief-relinquishment. It morally wrong (or epistemically irrational, or imprudent) to hold a belief on insufficient evidence. It morally right (or epistemically rational, or prudent) to believe on the basis of sufficient evidence, or to withhold belief in the perceived absence of evidence. It always obligatory to seek out all available epistemic evidence for a belief.” Ref

 Why are lies more appealing than the truth?

More on Null Hypothesis

In inferential statistics, the term “null hypothesis” is a general statement or default position that there is no relationship between two measured phenomena, or no association among groups. Rejecting or disproving the null hypothesis—and thus concluding that there are grounds for believing that there is a relationship between two phenomena (e.g. that a potential treatment has a measurable effect)—is a central task in the modern practice of science; the field of statistics gives precise criteria for rejecting a null hypothesis. The null hypothesis is generally assumed to be true until evidence indicates otherwise. In statistics, it is often denoted H0 (read “H-nought”, “H-null”, “H-oh”, or “H-zero”).

The concept of a null hypothesis is used differently in two approaches to statistical inference. In the significance testing approach of Ronald Fisher, a null hypothesis is rejected if the observed data are significantly unlikely to have occurred if the null hypothesis were true. In this case the null hypothesis is rejected and an alternative hypothesis is accepted in its place. If the data are consistent with the null hypothesis, then the null hypothesis is not rejected. In neither case is the null hypothesis or its alternative proven; the null hypothesis is tested with data and a decision is made based on how likely or unlikely the data are. This is analogous to the legal principle of presumption of innocence, in which a suspect or defendant is assumed to be innocent (null is not rejected) until proven guilty (null is rejected) beyond a reasonable doubt (to a statistically significant degree).

In the hypothesis testing approach of Jerzy Neyman and Egon Pearson, a null hypothesis is contrasted with an alternative hypothesis and the two hypotheses are distinguished on the basis of data, with certain error rates.

Proponents of each approach criticize the other approach. Nowadays, though, a hybrid approach is widely practiced and presented in textbooks. The hybrid is in turn criticized as incorrect and incoherent—for details, see Statistical hypothesis testing.

Statistical inference can be done without a null hypothesis, by specifying a statistical model corresponding to each candidate hypothesis and using model selection techniques to choose the most appropriate model. (The most common selection techniques are based on either Akaike information criterion or Bayes factor.)

Hypothesis testing requires constructing a statistical model of what the data would look like given that chance or random processes alone were responsible for the results. The hypothesis that chance alone is responsible for the results is called the null hypothesis. The model of the result of the random process is called the distribution under the null hypothesis. The obtained results are then compared with the distribution under the null hypothesis, and the likelihood of finding the obtained results is thereby determined.

Hypothesis testing works by collecting data and measuring how likely the particular set of data is, assuming the null hypothesis is true, when the study is on a randomly selected representative sample. The null hypothesis assumes no relationship between variables in the population from which the sample is selected.

If the data-set of a randomly selected representative sample is very unlikely relative to the null hypothesis (defined as being part of a class of sets of data that only rarely will be observed), the experimenter rejects the null hypothesis concluding it (probably) is false. This class of data-sets is usually specified via a test statistic which is designed to measure the extent of apparent departure from the null hypothesis. The procedure works by assessing whether the observed departure measured by the test statistic is larger than a value defined so that the probability of occurrence of a more extreme value is small under the null hypothesis (usually in less than either 5% or 1% of similar data-sets in which the null hypothesis does hold).

If the data do not contradict the null hypothesis, then only a weak conclusion can be made: namely, that the observed data set provides no strong evidence against the null hypothesis. In this case, because the null hypothesis could be true or false, in some contexts this is interpreted as meaning that the data give insufficient evidence to make any conclusion; in other contexts it is interpreted as meaning that there is no evidence to support changing from a currently useful regime to a different one.

For instance, a certain drug may reduce the chance of having a heart attack. Possible null hypotheses are “this drug does not reduce the chances of having a heart attack” or “this drug has no effect on the chances of having a heart attack”. The test of the hypothesis consists of administering the drug to half of the people in a study group as a controlled experiment. If the data show a statistically significant change in the people receiving the drug, the null hypothesis is rejected.

The null hypothesis and the alternate hypothesis are types of conjectures used in statistical tests, which are formal methods of reaching conclusions or making decisions on the basis of data. The hypotheses are conjectures about a statistical model of the population, which are based on a sample of the population. The tests are core elements of statistical inference, heavily used in the interpretation of scientific experimental data, to separate scientific claims from statistical noise.

“The statement being tested in a test of [statistical] significance is called the null hypothesis. The test of significance is designed to assess the strength of the evidence against the null hypothesis. Usually, the null hypothesis is a statement of ‘no effect’ or ‘no difference’.” It is often symbolized as H0.

The statement that is hoped or expected to be true instead of the null hypothesis is the alternative hypothesis. Symbols include H1and Ha.

Statistical significance test: “Very roughly, the procedure for deciding goes like this: Take a random sample from the population. If the sample data are consistent with the null hypothesis, then do not reject the null hypothesis; if the sample data are inconsistent with the null hypothesis, then reject the null hypothesis and conclude that the alternative hypothesis is true.”

Given the test scores of two random samples of men and women, does one group differ from the other? A possible null hypothesis is that the mean male score is the same as the mean female score:

H0μ1 = μ2


H0 = the null hypothesis,
μ1 = the mean of population 1, and
μ2 = the mean of population 2.

A stronger null hypothesis is that the two samples are drawn from the same population, such that the variances and shapes of the distributions are also equal.

Simple hypothesis
Any hypothesis which specifies the population distribution completely. For such a hypothesis the sampling distribution of any statistic is a function of the sample size alone.
Composite hypothesis
Any hypothesis which does not specify the population distribution completely. Example: A hypothesis specifying a normal distribution with a specified mean and an unspecified variance.

The simple/composite distinction was made by Neyman and Pearson.

Exact hypothesis
Any hypothesis that specifies an exact parameter value. Example: μ = 100. Synonym: point hypothesis.
Inexact hypothesis
Those specifying a parameter range or interval. Examples: μ ≤ 100; 95 ≤ μ ≤ 105.

Fisher required an exact null hypothesis for testing (see the quotations below).

one-tailed hypothesis (tested using a one-sided test) is an inexact hypothesis in which the value of a parameter is specified as being either:

  • above or equal to a certain value, or
  • below or equal to a certain value.

A one-tailed hypothesis is said to have directionality.

Fisher’s original (lady tasting tea) example was a one-tailed test. The null hypothesis was asymmetric. The probability of guessing all cups correctly was the same as guessing all cups incorrectly, but Fisher noted that only guessing correctly was compatible with the lady’s claim. (See the quotations below about his reasoning.)

Goals of null hypothesis tests

There are many types of significance tests for one, two or more samples, for means, variances and proportions, paired or unpaired data, for different distributions, for large and small samples; all have null hypotheses. There are also at least four goals of null hypotheses for significance tests:

  • Technical null hypotheses are used to verify statistical assumptions. For example, the residuals between the data and a statistical model cannot be distinguished from random noise. If true, there is no justification for complicating the model.
  • Scientific null assumptions are used to directly advance a theory. For example, the angular momentum of the universe is zero. If not true, the theory of the early universe may need revision.
  • Null hypotheses of homogeneity are used to verify that multiple experiments are producing consistent results. For example, the effect of a medication on the elderly is consistent with that of the general adult population. If true, this strengthens the general effectiveness conclusion and simplifies recommendations for use.
  • Null hypotheses that assert the equality of effect of two or more alternative treatments, for example, a drug and a placebo, are used to reduce scientific claims based on statistical noise. This is the most popular null hypothesis; It is so popular that many statements about significant testing assume such null hypotheses.

Rejection of the null hypothesis is not necessarily the real goal of a significance tester. An adequate statistical model may be associated with a failure to reject the null; the model is adjusted until the null is not rejected. The numerous uses of significance testing were well known to Fisher who discussed many in his book written a decade before defining the null hypothesis.

A statistical significance test shares much mathematics with a confidence interval. They are mutually illuminating. A result is often significant when there is confidence in the sign of a relationship (the interval does not include 0). Whenever the sign of a relationship is important, statistical significance is a worthy goal. This also reveals weaknesses of significance testing: A result can be significant without a good estimate of the strength of a relationship; significance can be a modest goal. A weak relationship can also achieve significance with enough data. Reporting both significance and confidence intervals is commonly recommended.

The varied uses of significance tests reduce the number of generalizations that can be made about all applications.

The choice of the null hypothesis is associated with sparse and inconsistent advice. Fisher mentioned few constraints on the choice and stated that many null hypotheses should be considered and that many tests are possible for each. The variety of applications and the diversity of goals suggests that the choice can be complicated. In many applications the formulation of the test is traditional. A familiarity with the range of tests available may suggest a particular null hypothesis and test. Formulating the null hypothesis is not automated (though the calculations of significance testing usually are). Sir David Cox has said, “How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis”.

A statistical significance test is intended to test a hypothesis. If the hypothesis summarizes a set of data, there is no value in testing the hypothesis on that set of data. Example: If a study of last year’s weather reports indicates that rain in a region falls primarily on weekends, it is only valid to test that null hypothesis on weather reports from any other year. Testing hypotheses suggested by the data is circular reasoning that proves nothing; It is a special limitation on the choice of the null hypothesis.

A routine procedure is as follows: Start from the scientific hypothesis. Translate this to a statistical alternative hypothesis and proceed: “Because Ha expresses the effect that we wish to find evidence for, we often begin with Ha and then set up H0 as the statement that the hoped-for effect is not present.” This advice is reversed for modeling applications where we hope not to find evidence against the null.

A complex case example is as follows: The gold standard in clinical research is the randomized placebo-controlled double-blind clinical trial. But testing a new drug against a (medically ineffective) placebo may be unethical for a serious illness. Testing a new drug against an older medically effective drug raises fundamental philosophical issues regarding the goal of the test and the motivation of the experimenters. The standard “no difference” null hypothesis may reward the pharmaceutical company for gathering inadequate data. “Difference” is a better null hypothesis in this case, but statistical significance is not an adequate criterion for reaching a nuanced conclusion which requires a good numeric estimate of the drug’s effectiveness. A “minor” or “simple” proposed change in the null hypothesis ((new vs old) rather than (new vs placebo)) can have a dramatic effect on the utility of a test for complex non-statistical reasons.

The choice of null hypothesis (H0) and consideration of directionality (see “one-tailed test“) is critical.

Tailedness of the null-hypothesis test

Consider the question of whether a tossed coin is fair (i.e. that on average it lands heads up 50% of the time) and an experiment where you toss the coin 5 times. A possible result of the experiment that we consider here is 5 heads. Let outcomes be considered unlikely with respect to an assumed distribution if their probability is lower than a significance threshold of 0.05.

A potential null hypothesis implying a one-tail test is “this coin is not biased toward heads”. Beware that, in this context, the word “tail” takes two meanings: either as outcome of a single toss, or as region of extremal values in a probability distribution.

Indeed, with a fair coin the probability of this experiment outcome is 1/25=0.031, which would be even lower if the coin were biased in favour of tails. Therefore, the observations are not likely enough for the null hypothesis to hold, and the test refutes it. Since the coin is ostensibly neither fair nor biased toward tails, the conclusion of the experiment is that the coin is biased towards heads.

Alternatively, a null hypothesis implying a two-tailed test is “this coin is fair”. This one null hypothesis could be examined by looking out for either too many tails or too many heads in the experiments. The outcomes that would tend to refuse this null hypothesis are those with a large number of heads or a large number of tails, and our experiment with 5 heads would seem to belong to this class.

However, the probability of 5 tosses of the same kind, irrespective of whether these are head or tails, is twice as much as that of the 5-head occurrence singly considered. Hence, under this two-tailed null hypothesis, the observation receives a probability value of 0.061. Hence again, with the same significance threshold used for the one-tailed test (0.05), the same outcome is not statistically significant. Therefore, the two-tailed null hypothesis will be preserved in this case, not supporting the conclusion that the coin is biased towards heads reached with the single-tailed null hypothesis.

This example illustrates that the conclusion reached from a statistical test may depend on the precise formulation of the null and alternative hypotheses.

Fisher said, “the null hypothesis must be exact, that is free of vagueness and ambiguity, because it must supply the basis of the ‘problem of distribution,’ of which the test of significance is the solution”, implying a more restrictive domain for H0. According to this view, the null hypothesis must be numerically exact—it must state that a particular quantity or difference is equal to a particular number. In classical science, it is most typically the statement that there is no effect of a particular treatment; in observations, it is typically that there is no difference between the value of a particular measured variable and that of a prediction.

Most statisticians believe that it is valid to state direction as a part of null hypothesis, or as part of a null hypothesis/alternative hypothesis pair. However, the results are not a full description of all the results of an experiment, merely a single result tailored to one particular purpose. For example, consider an H0 that claims the population mean for a new treatment is an improvement on a well-established treatment with population mean = 10 (known from long experience), with the one-tailed alternative being that the new treatment’s mean > 10. If the sample evidence obtained through x-bar equals −200 and the corresponding t-test statistic equals −50, the conclusion from the test would be that there is no evidence that the new treatmnent is better than the existing one: it would not report that it is markedly worse, but that is not what this particular test is looking for. To overcome any possible ambiguity in reporting the result of the test of a null hypothesis, it is best to indicate whether the test was two-sided and, if one-sided, to include the direction of the effect being tested.

The statistical theory required to deal with the simple cases of directionality dealt with here, and more complicated ones, makes use of the concept of an unbiased test.

The directionality of hypotheses is not always obvious. The explicit null hypothesis of Fisher’s Lady tasting tea example was that the Lady had no such ability, which led to a symmetric probability distribution. The one-tailed nature of the test resulted from the one-tailed alternate hypothesis (a term not used by Fisher). The null hypothesis became implicitly one-tailed. The logical negation of the Lady’s one-tailed claim was also one-tailed. (Claim: Ability > 0; Stated null: Ability = 0; Implicit null: Ability ≤ 0).

Pure arguments over the use of one-tailed tests are complicated by the variety of tests. Some tests (for instance the χ2 goodness of fit test) are inherently one-tailed. Some probability distributions are asymmetric. The traditional tests of 3 or more groups are two-tailed.

Advice concerning the use of one-tailed hypotheses has been inconsistent and accepted practice varies among fields. The greatest objection to one-tailed hypotheses is their potential subjectivity. A non-significant result can sometimes be converted to a significant result by the use of a one-tailed hypothesis (as the fair coin test, at the whim of the analyst). The flip side of the argument: One-sided tests are less likely to ignore a real effect. One-tailed tests can suppress the publication of data that differs in sign from predictions. Objectivity was a goal of the developers of statistical tests.

It is a common practice to use a one-tailed hypotheses by default. However, “If you do not have a specific direction firmly in mind in advance, use a two-sided alternative. Moreover, some users of statistics argue that we should always work with the two-sided alternative.”

One alternative to this advice is to use three-outcome tests. It eliminates the issues surrounding directionality of hypotheses by testing twice, once in each direction and combining the results to produce three possible outcomes. Variations on this approach have a history, being suggested perhaps 10 times since 1950.

Disagreements over one-tailed tests flow from the philosophy of science. While Fisher was willing to ignore the unlikely case of the Lady guessing all cups of tea incorrectly (which may have been appropriate for the circumstances), medicine believes that a proposed treatment that kills patients is significant in every sense and should be reported and perhaps explained. Poor statistical reporting practices have contributed to disagreements over one-tailed tests. Statistical significance resulting from two-tailed tests is insensitive to the sign of the relationship; Reporting significance alone is inadequate. “The treatment has an effect” is the uninformative result of a two-tailed test. “The treatment has a beneficial effect” is the more informative result of a one-tailed test. “The treatment has an effect, reducing the average length of hospitalization by 1.5 days” is the most informative report, combining a two-tailed significance test result with a numeric estimate of the relationship between treatment and effect. Explicitly reporting a numeric result eliminates a philosophical advantage of a one-tailed test. An underlying issue is the appropriate form of an experimental science without numeric predictive theories: A model of numeric results is more informative than a model of effect signs (positive, negative or unknown) which is more informative than a model of simple significance (non-zero or unknown); in the absence of numeric theory signs may suffice.

History of statistical tests

Statistical hypothesis testing § Origins and early controversy

The history of the null and alternative hypotheses is embedded in the history of statistical tests.

  • Before 1925: There are occasional transient traces of statistical tests for centuries in the past, which provide early examples of null hypotheses. In the late 19th century statistical significance was defined. In the early 20th century important probability distributions were defined. Gossett and Pearson worked on specific cases of significance testing.
  • 1925: Fisher published the first edition of Statistical Methods for Research Workers which defined the statistical significance test and made it a mainstream method of analysis for much of experimental science. The text was devoid of proofs and weak on explanations, but it was filled with real examples. It placed statistical practice in the sciences well in advance of published statistical theory.
  • 1933: In a series of papers (published over a decade starting in 1928) Neyman & Pearson defined the statistical hypothesis test as a proposed improvement on Fisher’s test. The papers provided much of the terminology for statistical tests including alternative hypothesis and H0 as a hypothesis to be tested using observational data (with H1, H2… as alternatives). Neyman did not use the term null hypothesis in later writings about his method.
  • 1935: Fisher published the first edition of the book “The Design of Experiments” which introduced the null hypothesis (by example rather than by definition) and carefully explained the rationale for significance tests in the context of the interpretation of experimental results; see The Design of Experiments#Quotations regarding the null hypothesis.
  • Following: Fisher and Neyman quarreled over the relative merits of their competing formulations until Fisher’s death in 1962. Career changes and World War II ended the partnership of Neyman and Pearson. The formulations were merged by relatively anonymous textbook writers, experimenters (journal editors) and mathematical statisticians without input from the principals. The subject today combines much of the terminology and explanatory power of Neyman & Pearson with the scientific philosophy and calculations provided by Fisher. Whether statistical testing is properly one subject or two remains a source of disagreement. Sample of two: One text refers to the subject as hypothesis testing (with no mention of significance testing in the index) while another says significance testing (with a section on inference as a decision). Fisher developed significance testing as a flexible tool for researchers to weigh their evidence. Instead, testing has become institutionalized. Statistical significance has become a rigidly defined and enforced criterion for the publication of experimental results in many scientific journals. In some fields, significance testing has become the dominant and nearly exclusive form of statistical analysis. As a consequence, the limitations of the tests have been exhaustively studied. Books have been filled with the collected criticism of significance testing.

The Null Hypothesis (This post was originally posted at Disjointed Thinking.)

One of the most accurate ways to describe my religious beliefs (or lack thereof) is by way of a concept known as the “null hypothesis”. Like most atheists, I do not claim that I know God does not exist. I merely claim that there is not enough evidence to justify belief in God. And the best way to illustrate this claim is through the null hypothesis. This is a statistical concept that is used for hypothesis testing in science. Because statistics is not a strong point for many people, I will try to explain it using a minimum of stats jargon; however, some will be required, and I will try to explain what each term means the best that I can. I really feel that this is an important concept to understand when one is trying to assess evidence claims (which happens to us all the time). So hang on for the ride!

Basic Statistics

When I measure a certain phenomenon, such as people’s height, there will always be some variability in the results. And for many phenomena, this variability will result in a bell-shaped curve known as a “normal distribution”. There are some very interesting properties of the normal distribution, but what is important here is that the majority of the cases will be clustered around the mean (the statistical word for “average”), and then fewer cases will occur further away from the mean. So for instance, the mean height for a Canadian adult is about 5’8″ for men and 5’3″ for women.1 So if I randomly selected a Canadian man, he would be most likely to have a height somewhere around 5’8″. Or if I wanted to be more precise, I could say that he is likely to fall between about 4’11” and 6’5″. I’d be very surprised if this randomly selected person turned out to be 8 feet tall. There are very few people who are that tall, so it would be very unusual to have chosen them. So the normal distribution tells us that the most frequent cases occur around the mean, and that cases occurring further away are less frequent.

Hypothesis Testing

So what would I do if someone told me of a town in rural Alberta where all the adult townspeople were over 7 feet tall? I would likely be very skeptical. Such a thing is very unlikely, isn’t it? It’s unusual to find even one person over 7 feet tall, let alone an entire town of both men and women over 7 feet tall! But to be a good scientist, I should hold onto my skepticism but remain open to the possibility that such a strange case is indeed true. I would want to head over to this town and start measuring. But let’s say I don’t have the resources to measure every single individual in the town. I might measure 40 or 50 of them. And perhaps the person who told me this story was exaggerating a little bit, and some were below 7 feet. But I might still ask, “Are the people in this town significantly taller than the general population?” And that is a great question with which to use hypothesis testing.

When scientists want to test a hypothesis, they must come up with two different hypotheses to compare. One of these (known as the alternate hypothesis) is the one they would ideally like to be confirmed; in my case, it is this: “The people in this rural town are statistically taller than the general population.” The other hypothesis is known as the null hypothesis, and in my case it would be the following: “There is no difference in the average height of the people in this rural town and the general population.” But why do we need to compare these two things? Why not just see if the alternate hypothesis is confirmed? The reason is because, like I mentioned earlier, phenomena always have some variability. If I measured the townspeople and found out that they averaged 6’8″, that doesn’t mean that every single person was 6’8″ tall. Some were taller, and some were shorter. So how would I know whether I was finding someactual difference between the townspeople and the general population, or if I just happened to select the 50 tallest people in the town? Hypothesis testing helps us to determine whether we just have a bad sample, or if the normal distribution for the town is actually different from the normal distribution for the general population. Or to put it another way, it helps us distinguish between real differences and random fluctuation. The null hypothesis says, “This is just random fluctuation,” and the alternate hypothesis says, “No, this is a real difference.” Scientists support the alternate hypothesis indirectly by disconfirming the null hypothesis. If the data that scientists collect don’t fit with the null hypothesis, then they have better evidence to support the idea that the alternate hypothesis is true. On the other hand, if the data do fit with the null hypothesis, this doesn’t necessarily mean that the null hypothesis is true; it just means that the data do not contradict it.

The Role of Evidence

Another way to look at the concept of the null hypothesis is to use the analogy of a court case. In the modern justice system, the defendant is considered innocent until proven guilty. The prosecution must provide sufficient evidence to prove his or her guilt. In some ways, this works similar to the null hypothesis. The null hypothesis is assumed to be true until sufficient evidence is provided to demonstrate otherwise. The evidence in favour of the alternate hypothesis must outweigh the evidence in favour of the null hypothesis.2 If I want to prove the statement that the Albertan townspeople are significantly taller than the general population, I have to have sufficient evidence that my measurements are not just a reflection of random variability and then add more evidence suggesting that they are actually due to some real difference in height (whatever the cause of this unusually tall town might be).

The null hypothesis works very similarly when it comes to other types of claims. The statement, “God exists”, is a positive claim about the existence of an entity. So in this case, the null hypothesis would be, “God does not exist.” This works the same as any other positive claim: “Unicorns exist” vs. “Unicorns do not exist”, “It rained yesterday” vs. “It did not rain yesterday”, “Black swans exist” vs. “Black swans do not exist”, and so on. If one wants to prove the truth of these statements, they must provide evidence which is sufficiently inconsistent with the null hypothesis. If I try to prove that it rained yesterday by saying that I held my hand out the window and felt splashes of water, this might be good evidence, unless someone points out that I have a leaky eaves-trough directly above my window. In other words, if my evidence can be shown to be perfectly consistent with the idea that it did not, in fact, rain yesterday, then it should not be used to support the idea that it did rain yesterday. I should provide other evidence to support my claim. Or if I mention that I saw a black swan, but upon further examination it turns out to be a black duck, this is no longer evidence that black swans exist. The fact that the evidence must be thrown out does not prove that black swans don’t exist, but it no longer proves that they do. And if there were absolutely no good evidence to prove that black swans exist, why would anyone believe in them?

The claim that God exists is exactly the same as all these other claims. The burden of proof is always on the person making the positive claim. That person must provide good evidence to demonstrate that the claim is true. And if the evidence they provide is entirely consistent with the null hypothesis (that God does not exist), then the evidence is no good. Again, this does not prove that God does not exist, but if there is absolutely no good evidence to prove that God exists, why would anyone believe in him? This is why the null hypothesis is a crucial concept to grasp. Saying “The evidence does not prove that God exists” is entirely different than saying “The evidence proves that God does not exist”. The null hypothesis is a safe bet that requires other hypotheses to prove themselves to be true before one believes in them. If I went to a random rural town in Alberta, my first assumption would be that their average height is about the same as the average height of the general population. This doesn’t mean that it actually is the case that their average heights are the same, but I have no prior reason to think so until I have evidence to prove otherwise. It would be silly to point to an entirely random town for which one has no prior knowledge and say, “That town is filled with giants.” And it would be even sillier to take measurements of the townspeople, see that they have an average height similar to the general population, and still say, “That town is filled with giants.” As far as I am concerned, however, that is what most believers in God do.


I don’t intend to get into an examination of the evidence for and against the existence of God. I have written an entire ten-part series about just that. The intent of this article was to develop a process for assessing whether God exists. The general process is to assume that God does not exist until one finds sufficient evidence to support the claim that he does. To do otherwise is to choose a position that one likes before even taking a look at the evidence. (It’s like assuming the town is full of giants before even seeing a townsperson.) In many cases where this happens, people do this for emotional reasons. They have some desire to believe a certain thing, and so they make up their mind before they even take a look at the evidence. Such a process is irrational, especially since humans have “tendencies”. We often see patterns that are not there (like people who develop superstitions, for example); we seek out evidence to support the beliefs that we already hold and ignore disconfirming evidence, instead of trying to objectively assess all the evidence; and we often reach conclusions that we like and then make up justifications for them afterwards. The only way to avoid these tendencies is to acknowledge that we suffer from them and then try to minimize them through the use of rigorous processes such as hypothesis testing.

So when I say that I am a “null hypothesis atheist”, I mean that the evidence for God’s existence is either a) faulty, b) illusory, or c) consistent with the null hypothesis that God does not exist. I do not make the statement, “Therefore, God does not exist.” Instead, I simply say, “There is no good evidence to believe that God exists, and I don’t believe in things with no good evidence.” Sure, God might be out there, hiding behind some distant spiral galaxy or outside of space and time altogether. But I will take the conservative path that tries to minimize irrationality, because such a path is the best process we’ve developed to distinguish fact from fiction.


  1. I should note that human height is not technically normally distributed, but rather negatively skewed. I’m using this example for illustrative purposes.
  2. Any statisticians reading this are probably cringing at this point. I understand that this is a distortion of how hypothesis testing actually works. But as an admittedly imperfect analogy, I still think it works well enough for providing non-statisticians with an understanding of the basic reasoning behind the null/alternate hypotheses.

My thoughts on Religion Evolution with external links for more info:

“Religion is an Evolved Product” and Yes, Religion is Like Fear Given Wings…

Atheists talk about gods and religions for the same reason doctors talk about cancer, they are looking for a cure, or a firefighter talks about fires because they burn people and they care to stop them. We atheists too often feel a need to help the victims of mental slavery, held in the bondage that is the false beliefs of gods and the conspiracy theories of reality found in religions.

“Understanding Religion Evolution: Animism, Totemism, Shamanism, Paganism & Progressed organized religion”

Understanding Religion Evolution:

“An Archaeological/Anthropological Understanding of Religion Evolution”

It seems ancient peoples had to survived amazing threats in a “dangerous universe (by superstition perceived as good and evil),” and human “immorality or imperfection of the soul” which was thought to affect the still living, leading to ancestor worship. This ancestor worship presumably led to the belief in supernatural beings, and then some of these were turned into the belief in gods. This feeble myth called gods were just a human conceived “made from nothing into something over and over, changing, again and again, taking on more as they evolve, all the while they are thought to be special,” but it is just supernatural animistic spirit-belief perceived as sacred.

Quick Evolution of Religion?

Pre-Animism (at least 300,000 years ago). So, it all starts in a general way with Animism (such as that seen in Africa: 100,000 years ago) (theoretical belief in supernatural powers/spirits), then this is physically expressed in or with Totemism (Europe: 50,000 years ago) (theoretical belief in mythical relationship with powers/spirits through a totem item), which then enlists a full-time specific person to do this worship and believed interacting Shamanism (beginning around 30,000 years ago in Siberia) (theoretical belief in access and influence with spirits through ritual), and then there is the further employment of myths and gods added to all the above giving you Paganism (beginning around 12,000 years ago in Turkey) (often a lot more nature-based than most current top world religions, thus hinting to their close link to more ancient religious thinking it stems from). My hypothesis is expressed with an explanation of the building of a theatrical house (modern religions development). Progressed organized religion (around 5,000 years ago as sen in Egypt) with CURRENT “World” RELIGIONS (after 4,000 years ago).

Historically, in large city-state societies (such as Egypt or Iraq) starting around 5,000 years ago culminated to make religion something kind of new, a sociocultural-governmental-religious monarchy, where all or at least many of the people of such large city-state societies seem familiar with and committed to the existence of “religion” as the integrated life identity package of control dynamics with a fixed closed magical doctrine, but this juggernaut integrated religion identity package of Dogmatic-Propaganda certainly did not exist or if developed to an extent it was highly limited in most smaller prehistoric societies as they seem to lack most of the strong control dynamics with a fixed closed magical doctrine (magical beliefs could be at times be added or removed). Many people just want to see developed religious dynamics everywhere even if it is not. Instead, all that is found is largely fragments until the domestication of religion.

Religions, as we think of them today, are a new fad, even if they go back to around 6,000 years in the timeline of human existence, this amounts to almost nothing when seen in the long slow evolution of religion at least around 70,000 years ago with one of the oldest ritual worship. Stone Snake of South Africa: “first human worship” 70,000 years ago. This message of how religion and gods among them are clearly a man-made thing that was developed slowly as it was invented and then implemented peace by peace discrediting them all. Which seems to be a simple point some are just not grasping how devastating to any claims of truth when we can see the lie clearly in the archeological sites.

I wish people fought as hard for the actual values as they fight for the group/clan names political or otherwise they think support values. Every amount spent on war is theft to children in need of food or the homeless kept from shelter.

Here are several of my blog posts on history:

I am not an academic. I am a revolutionary that teaches in public, in places like social media, and in the streets. I am not a leader by some title given but from my commanding leadership style of simply to start teaching everywhere to everyone, all manner of positive education.

Art by Damien Marie AtHope

While hallucinogens are associated with shamanism, it is alcohol that is associated with paganism.

The Atheist-Humanist-Leftist Revolutionaries Shows in the prehistory series:

Show one: Prehistory: related to “Anarchism and Socialism” the division of labor, power, rights, and recourses.

Show two: Pre-animism 300,000 years old and animism 100,000 years old: related to “Anarchism and Socialism”

Show tree: Totemism 50,000 years old: related to “Anarchism and Socialism”

Show four: Shamanism 30,000 years old: related to “Anarchism and Socialism”

Show five: Paganism 12,000 years old: related to “Anarchism and Socialism”

Show six: Emergence of hierarchy, sexism, slavery, and the new male god dominance: Paganism 7,000-5,000 years old: related to “Anarchism and Socialism” (Capitalism) (World War 0) Elite and their slaves!

Show seven: Paganism 5,000 years old: progressed organized religion and the state: related to “Anarchism and Socialism” (Kings and the Rise of the State)

Show eight: Paganism 4,000 years old: Moralistic gods after the rise of Statism and often support Statism/Kings: related to “Anarchism and Socialism” (First Moralistic gods, then the Origin time of Monotheism)

Prehistory: related to “Anarchism and Socialism” the division of labor, power, rights, and recourses: VIDEO

Pre-animism 300,000 years old and animism 100,000 years old: related to “Anarchism and Socialism”: VIDEO

Totemism 50,000 years old: related to “Anarchism and Socialism”: VIDEO

Shamanism 30,000 years old: related to “Anarchism and Socialism”: VIDEO

Paganism 12,000 years old: related to “Anarchism and Socialism” (Pre-Capitalism): VIDEO

Paganism 7,000-5,000 years old: related to “Anarchism and Socialism” (Capitalism) (World War 0) Elite and their slaves: VIEDO

Paganism 5,000 years old: progressed organized religion and the state: related to “Anarchism and Socialism” (Kings and the Rise of the State): VIEDO

Paganism 4,000 years old: related to “Anarchism and Socialism” (First Moralistic gods, then the Origin time of Monotheism): VIEDO

I do not hate simply because I challenge and expose myths or lies any more than others being thought of as loving simply because of the protection and hiding from challenge their favored myths or lies.

The truth is best championed in the sunlight of challenge.

An archaeologist once said to me “Damien religion and culture are very different”

My response, So are you saying that was always that way, such as would you say Native Americans’ cultures are separate from their religions? And do you think it always was the way you believe?

I had said that religion was a cultural product. That is still how I see it and there are other archaeologists that think close to me as well. Gods too are the myths of cultures that did not understand science or the world around them, seeing magic/supernatural everywhere.

I personally think there is a goddess and not enough evidence to support a male god at Çatalhöyük but if there was both a male and female god and goddess then I know the kind of gods they were like Proto-Indo-European mythology.

*Next is our series idea that was addressed in, Anarchist Teaching as Free Public Education or Free Education in the Public: VIDEO

Our future video series: Organized Oppression: Mesopotamian State Force and the Politics of power (9,000-4,000 years ago) adapted from: The Complete and Concise History of the Sumerians and Early Bronze Age Mesopotamia (7000-2000 BC): https://www.youtube.com/watch?v=szFjxmY7jQA

Show #1: Mesopotamian State Force and the Politics of Power (Samarra, Halaf, Ubaid)

Show #2: Mesopotamian State Force and the Politics of Power (Eridu “Tell Abu Shahrain”)

Show #3: Mesopotamian State Force and the Politics of Power (Uruk and the First Cities)

Show #4: Mesopotamian State Force and the Politics of Power (First Kings)

Show #5: Mesopotamian State Force and the Politics of Power (Early Dynastic Period)

Show #6: Mesopotamian State Force and the Politics of Power (King/Ruler Lugalzagesi)

Show #7: Mesopotamian State Force and the Politics of Power (Sargon and Akkadian Rule)

Show #8: Mesopotamian State Force and the Politics of Power (Naram-Sin, Post-Akkadian Rule, and the Gutians)

Show #9: Mesopotamian State Force and the Politics of Power (Gudea of Lagash and Utu-hegal)

Show #10: Mesopotamian State Force and the Politics of Power (Third Dynasty of Ur / Neo-Sumerian Empire)

Show #11: Mesopotamian State Force and the Politics of Power (Amorites, Elamites, and the End of an Era)

Show #12: Mesopotamian State Force and the Politics of Power (Aftermath and Legacy of Sumer)

Art by Damien Marie AtHope

The “Atheist-Humanist-Leftist Revolutionaries”

Cory Johnston ☭ Ⓐ Atheist Leftist @Skepticallefty & I (Damien Marie AtHope) @AthopeMarie (my YouTube & related blog) are working jointly in atheist, antitheist, antireligionist, antifascist, anarchist, socialist, and humanist endeavors in our videos together, generally, every other Saturday.

Why Does Power Bring Responsibility?

Think, how often is it the powerless that start wars, oppress others, or commit genocide? So, I guess the question is to us all, to ask, how can power not carry responsibility in a humanity concept? I know I see the deep ethical responsibility that if there is power their must be a humanistic responsibility of ethical and empathic stewardship of that power. Will I be brave enough to be kind? Will I possess enough courage to be compassionate? Will my valor reached its height of empathy? I as everyone earns our justified respect by our actions, that are good, ethical, just, protecting, and kind. Do I have enough self-respect to put my love for humanity’s flushing, over being brought down by some of its bad actors? May we all be the ones doing good actions in the world, to help human flourishing.

I create the world I want to live in, striving for flourishing. Which is not a place but a positive potential involvement and promotion; a life of humanist goal precision. To master oneself, also means mastering positive prosocial behaviors needed for human flourishing. I may have lost a god myth as an atheist but I am happy to tell you my friend, it is exactly because of that, leaving the mental terrorizer, god belief that I truly regained my connected ethical as well as kind humanity.

Cory and I will talk about prehistory and theism, addressing the relevance to atheism, anarchism, and socialism.

At the same time of the rise of the male god 7,000 years ago was also the very time there was the rise of violence war, and clans to kingdoms, then empires, then states. It is all connected back to 7,000 years ago and it mover across the world.

Cory Johnston: https://damienmarieathope.com/2021/04/cory-johnston-mind-of-a-skeptical-leftist/?v=32aec8db952d  

The Mind of a Skeptical Leftist (YouTube)

Cory Johnston: Mind of a Skeptical Leftist 

The Mind of a Skeptical Leftist By Cory Johnston:   “Promoting critical thinking, social justice, and left-wing politics by covering current events and talking to a variety of people. Cory Johnston has been thoughtfully talking to people and attempting to promote critical thinking, social justice, and left-wing politics.”

Cory Johnston ☭ Ⓐ @Skepticallefty Evidence-based atheist leftist (he/him) Producer, host, and co-host of 4 podcasts @skeptarchy @skpoliticspod and @AthopeMarie


He needs our support. We rise by helping each other.

Damien Marie AtHope (“At Hope”) Axiological Atheist, Anti-theist, Anti-religionist, Secular Humanist. Rationalist, Writer, Artist, Poet, Philosopher, Advocate, Activist, Psychology, and Armchair Archaeology/Anthropology/Historian.

Damien is interested in: Freedom, Liberty, Justice, Equality, Ethics, Humanism, Science, Atheism, Antiteism, Antireligionism, Ignosticism, Left-Libertarianism, Anarchism, Socialism, Mutualism, Axiology, Metaphysics, LGBTQI, Philosophy, Advocacy, Activism, Mental Health, Psychology, Archaeology, Social Work, Sexual Rights, Marriage Rights, Woman’s Rights, Gender Rights, Child Rights, Secular Rights, Race Equality, Ageism/Disability Equality, Etc. And a far-leftist, “Anarcho-Humanist.”

Art by Damien Marie AtHope

Damien Marie AtHope (Said as “At” “Hope”)/(Autodidact Polymath but not good at math):

Axiological Atheist, Anti-theist, Anti-religionist, Secular Humanist, Rationalist, Writer, Artist, Jeweler, Poet, “autodidact” Philosopher, schooled in Psychology, and “autodidact” Armchair Archaeology/Anthropology/Pre-Historian (Knowledgeable in the range of: 1 million to 5,000/4,000 years ago). I am an anarchist socialist politically. Reasons for or Types of Atheism

My Website, My Blog, My (free accesses) Patreon, My (free accesses) Patreon Blog & Short-writing or Quotes  My YouTube, Twitter: @AthopeMarie, and My Email: damien.marie.athope@gmail.com

Pin It on Pinterest

Share This