1574: Trouble for Science - Revision history

90.209.85.113: /* Explanation */ Used correct word

2025-08-15T08:19:11Z

‎Explanation: Used correct word

DKMell: minor fixes

2025-07-22T23:05:32Z

minor fixes

172.69.17.54: another explanation

2025-03-28T19:27:18Z

another explanation

Trambelus: /* Explanation */ add some info on immunoassays

2024-11-01T01:17:36Z

‎Explanation: add some info on immunoassays

1234231587678 at 20:25, 8 April 2024

2024-04-08T20:25:05Z

Natg19: /* Transcript */ rename

2022-08-09T01:44:09Z

‎Transcript: rename

Theusaf: Reverted edits by Donald Trump (talk) to last revision by CRLF

2022-05-26T19:58:56Z

Reverted edits by Donald Trump (talk) to last revision by CRLF

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Donald Trump: Reverted edit by anti-crap user

2022-05-26T19:04:05Z

Reverted edit by anti-crap user

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CRLF: Reverted vandalism with User:CRLF/OneClickUndo.js

2022-05-26T18:00:44Z

Reverted vandalism with User:CRLF/OneClickUndo.js

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Donald Trump: Crapped page

2022-05-26T17:08:59Z

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@@ Line 22: / Line 22: @@
 ''P''-values are used in hypothesis testing. The ''p''-value is the probability of observing an effect, result or relationship in your sample data, given that no such effect, result, or relationship exists in the population. It is based on the sample data and the particular statistic (such as sample average, or the ''t'' or ''F'' statistic). A statistic is the result of a calculation based on the sample. A ''p''-value can be calculated for each statistic of interest. Formally, the ''p''-value is the probability of observing a test statistic equal to or greater than the one based on the sample data, given that the null hypothesis is true.
-The threshold for ''p''-value cutoff, ''α'', is pre-specified (usually 5% or the more conservative 1%). When the ''p''-value is lower to or equal to ''α'', the null hypothesis is rejected in favor of the alternative hypothesis. When it is higher than ''α'', the null hypothesis is retained.
+The threshold for ''p''-value cutoff, ''α'', is pre-specified (usually 5% or the more conservative 1%). When the ''p''-value is lower than or equal to ''α'', the null hypothesis is rejected in favor of the alternative hypothesis. When it is higher than ''α'', the null hypothesis is retained.
 The value used for ''α'' [http://web.lru.dk/sites/lru.dk/files/lru/docs/kap9/kapitel_9_126_On_the_origins.pdf was proposed by] {{w|Ronald Fisher}} and is arbitrary.

@@ Line 15: / Line 15: @@
 Immunoassays are biochemical tests that leverage the specific binding between an antibody and its antigen to detect and quantify substances, typically proteins, hormones, or pathogens. These assays are foundational in various fields, including medical diagnostics, biotechnology research, environmental monitoring, and food safety testing. This statement about their unreliability is true: see Kebaneilwe Lebani, [http://espace.library.uq.edu.au/view/UQ:352531 Antibody Discovery for Development of a Serotyping Dengue Virus NS1 Capture Assay], 2014. In this Ph.D. thesis, 11 references are given.
-;Problems with the p-value as an indicator of significance
+;Problems with the ''p''-value as an indicator of significance
 In empirical research, one is usually interested in effects, results and relationships in a population. However, for practical reasons, only smaller subsets of populations, called samples, are available to the researcher. Usually, an effect of interest is tested using a sample. The purpose of hypothesis testing is to determine whether the observed effect (or lack of effect) in a sample is a random artifact of our particular sample, or whether there is a good chance that it also exists in the population.
 Generally, a null hypothesis states that there is no effect in the population while the alternative hypothesis states that there is an effect.
-P-values are used in hypothesis testing. The p-value is the probability of observing an effect, result or relationship in your sample data, given that no such effect, result, or relationship exists in the population. It is based on the sample data and the particular statistic (such as sample average, t or F). A statistic is the result of a calculation based on the sample. A p-value can be calculated for each statistic of interest. Formally, the p-value is the probability of observing a test statistic equal to or greater than the one based on the sample data, given that the null hypothesis is true.
+''P''-values are used in hypothesis testing. The ''p''-value is the probability of observing an effect, result or relationship in your sample data, given that no such effect, result, or relationship exists in the population. It is based on the sample data and the particular statistic (such as sample average, or the ''t'' or ''F'' statistic). A statistic is the result of a calculation based on the sample. A ''p''-value can be calculated for each statistic of interest. Formally, the ''p''-value is the probability of observing a test statistic equal to or greater than the one based on the sample data, given that the null hypothesis is true.
-The threshold for p-value cutoff, α, is pre-specified (usually 5% or 1%, which is more conservative). When the p-value is lower to or equal to α, the null hypothesis is rejected in favor of the alternative hypothesis. When it is higher than α, the null hypothesis is retained.
+The threshold for ''p''-value cutoff, ''α'', is pre-specified (usually 5% or the more conservative 1%). When the ''p''-value is lower to or equal to ''α'', the null hypothesis is rejected in favor of the alternative hypothesis. When it is higher than ''α'', the null hypothesis is retained.
-The value used for ''α'' has been proposed by [http://web.lru.dk/sites/lru.dk/files/lru/docs/kap9/kapitel_9_126_On_the_origins.pdf Fisher] and is arbitrary.
+The value used for ''α'' [http://web.lru.dk/sites/lru.dk/files/lru/docs/kap9/kapitel_9_126_On_the_origins.pdf was proposed by] {{w|Ronald Fisher}} and is arbitrary.
 ;Overfeeding of laboratory rodents compromises animal models
 [http://tpx.sagepub.com/content/24/6/757.full.pdf Keenan et al.] makes this case. Additionally, the word model takes on two meanings. In one sense, "model" can refer to a scientific description that makes sense of a phenomenon; in another sense, "model" can refer to an individual whose job it is to demonstrate fashions, typically fashionable outfits. Fashion models are notorious for being exceptionally thin, and so overfeeding would compromise their job as a model.
@@ Line 48: / Line 48: @@
 ;(Title text) Careful mathematical analysis demonstrates small-scale irregularities in Gaussian distribution
-This is another joke of a premise that is obviously untrue. The {{w|Gaussian function|Gaussian distribution}} is a mathematical construct that is generally known as the bell curve or the Normal distribution. As it is an ideal mathematical construction, by definition, it cannot have any irregularities - similar to how the equation y = 2x + 1 cannot have small-scale irregularities. The joke probably alludes to the fact that many types of observations are frequently initially modeled as a Gaussian distribution, though on careful observation the actual distribution of outcomes will often deviate from a pure Gaussian distribution. The joke could also refer to the fact that when a Gaussian distribution is plotted on a screen, small 1-pixel irregularities can become visible merely from the screen's pixel size; this is not an irregularity with the Gaussian distribution itself, but only with the plotting.
+The {{w|Gaussian function|Gaussian distribution}} is a mathematical construct that is generally known as the bell curve or the normal distribution. As it is an ideal mathematical construction by definition, it cannot have any irregularities - similar to how the equation ''y'' = 2''x'' + 1 cannot have small-scale irregularities. The joke probably alludes to the fact that many types of observations are frequently initially modeled as a Gaussian distribution, though on careful observation the actual distribution of outcomes will often deviate from a pure Gaussian distribution. The joke could also refer to the fact that when a Gaussian distribution is plotted on a screen, small 1-pixel irregularities can become visible merely from the screen's pixel size; this is not an irregularity with the Gaussian distribution itself, but only with the plotting.
 In addition, an experiment to test a Gaussian distribution will have a finite sample size, giving a non-exact Gaussian distribution. A possible paper submitted would conclude that this result is "approximately a normal distribution" with "small-scale irregularities". A news reporter without knowledge of statistics could easily misinterpret that this paper decisively concludes errors in the mathematical definitions (rather than coming from random error inherent in experimenting).

@@ Line 48: / Line 48: @@
 ;(Title text) Careful mathematical analysis demonstrates small-scale irregularities in Gaussian distribution
-This is another joke of a premise that is obviously untrue. The {{w|Gaussian function|Gaussian distribution}} is a mathematical construct that is generally known as the bell curve or the Normal distribution. As it is an ideal mathematical construction, by definition, it cannot have any irregularities - similar to how the equation y = 2x + 1 cannot have small-scale irregularities. The joke probably alludes to the fact that many types of observations are frequently initially modeled as a Gaussian distribution, though on careful observation the actual distribution of outcomes will often deviate from a pure Gaussian distribution.
+This is another joke of a premise that is obviously untrue. The {{w|Gaussian function|Gaussian distribution}} is a mathematical construct that is generally known as the bell curve or the Normal distribution. As it is an ideal mathematical construction, by definition, it cannot have any irregularities - similar to how the equation y = 2x + 1 cannot have small-scale irregularities. The joke probably alludes to the fact that many types of observations are frequently initially modeled as a Gaussian distribution, though on careful observation the actual distribution of outcomes will often deviate from a pure Gaussian distribution. The joke could also refer to the fact that when a Gaussian distribution is plotted on a screen, small 1-pixel irregularities can become visible merely from the screen's pixel size; this is not an irregularity with the Gaussian distribution itself, but only with the plotting.
 In addition, an experiment to test a Gaussian distribution will have a finite sample size, giving a non-exact Gaussian distribution. A possible paper submitted would conclude that this result is "approximately a normal distribution" with "small-scale irregularities". A news reporter without knowledge of statistics could easily misinterpret that this paper decisively concludes errors in the mathematical definitions (rather than coming from random error inherent in experimenting).

@@ Line 13: / Line 13: @@
 ;Many commercial antibody-based immunoassays are unreliable
-This sentence is true. See Kebaneilwe Lebani, [http://espace.library.uq.edu.au/view/UQ:352531 Antibody Discovery for Development of a Serotyping Dengue Virus NS1 Capture Assay], 2014. In this Ph.D. thesis, 11 references are given.
+Immunoassays are biochemical tests that leverage the specific binding between an antibody and its antigen to detect and quantify substances, typically proteins, hormones, or pathogens. These assays are foundational in various fields, including medical diagnostics, biotechnology research, environmental monitoring, and food safety testing. This statement about their unreliability is true: see Kebaneilwe Lebani, [http://espace.library.uq.edu.au/view/UQ:352531 Antibody Discovery for Development of a Serotyping Dengue Virus NS1 Capture Assay], 2014. In this Ph.D. thesis, 11 references are given.
 ;Problems with the p-value as an indicator of significance

@@ Line 41: / Line 41: @@
 The theme of this comic is that commonly accepted scientific methods can be unreliable, and the joke here is that a Bunsen burner, a device intended to heat things, is newly discovered to always cool things instead, which would be absurd.
-In theory, yes, putting a Bunsen Burner underneath an object that's already incredibly hot would, slowly, equalize the temperature between the flame and object resulting in cooling. Given that a Bunsen Burner burns between 1000 K and 2000 K, there is probably some methodological error if the testing materials were already much hotter than the flame (more than 2000 Kelvin). It's also possible that if the "controlled trial" involved a Bunsen burner that was not lit, but was turned on to allow gas to flow, it would have a cooling effect as the gas expanded from the line pressure to atmospheric pressure. Another alternative theory is that a cold substance, such as cold water or frigid air, was fed through the burner against a warmer object.
+In theory, yes, putting a Bunsen Burner underneath an object that's already incredibly hot would, slowly, equalize the temperature between the flame and object resulting in cooling. Given that a Bunsen Burner burns between 1000 {{w|Kelvin|K}} and 2000 K, there is probably some methodological error if the testing materials were already much hotter than the flame (more than 2000 K). It's also possible that if the "controlled trial" involved a Bunsen burner that was not lit, but was turned on to allow gas to flow, it would have a cooling effect as the gas expanded from the line pressure to atmospheric pressure. Another alternative theory is that a cold substance, such as cold water or frigid air, was fed through the burner against a warmer object.
 Alternatively, a trial could be set up to test something against a Bunsen burner on the one hand, and an even hotter flame on the other hand. As compared to that hotter flame, the Bunsen burner would not heat up the tested material as much, resulting in something being made "colder" than the alternative.

@@ Line 72: / Line 72: @@
 [[Category:Physics]]
 [[Category:Statistics]]
-[[Category:Research Papers]]
+[[Category:Scientific research]]