origins of the prohibition of pork consumption: a note

ORIGINS OF THE PROHIBITION OF PORK CONSUMPTION: A NOTE JAMAL MUNSHI ABSTRACT: The Biblical prohibition of pork consumption has motivated archaeologists to look for clues in the pig fraction of the faunal remains of farm animals to identify the time and place of its inception so that corresponding socio-political events may be used to determine how and why this prohibition came about. Although significant advances have been made in the archeozoology of pigs in recent years (Sapir-Hen, 2013), the statistical analysis of pig fractions in faunal data still follows Caroline Grigson’s set of arbitrary heuristics (Grigson, 1982). These heuristics do not adequately address the uncertainty in the data. This note proposes an 1 alternative analysis of pig fractions in faunal remains that improves statistical confidence in the conclusions .

1. INTRODUCTION Avoidance of certain specified foods has been a part of religious dogma for all of recorded history (Simoons, 1994) and a prominent part of this pattern is the pig taboo in Judaism (Rosenblum, 2010). The Jewish scriptures describe the pig as an unclean animal and forbid pork consumption2 in Leviticus (the pig, though it has a divided hoof, does not chew the cud; it is unclean for you) and again in Deuteronomy (the pig is also unclean although it has a divided hoof, it does not chew the cud. You are not to eat their meat or touch their carcasses). Scholars engaged in research in this area look for a sociopolitical rationale for the prohibition in terms of forming political and ethnic identities. Specifically, researchers would like to determine whether the prohibition of pork consumption was yet another attempt by the Biblical authors to unite the northern Israelites and the southern Judahites by creating a common identity (Finkelstein, 2002) (Sapir-Hen, 2013). This note is a meta-study of the paper on this topic by Sapir-Hen et al (Sapir-Hen, 2013). In it we examine this paper in some detail and look at the methodology employed in the analysis of the data and the conclusions derived from the analysis. The emphasis of this note is on certain statistical issues such as the uncertainty contained in the sample data and the matter of data aggregation from very different sample sizes. The proposed methodology improves the clarity of the data presentation and the reliability of the findings.

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Date: November, 2014. Keywords and phrases: archaeozoology, zooarchaeology, pigs, pork, archaeology, biblical archaeology, pig taboo, Judaism, Torah, Bible, Israel Finkelstein, Levant, Philistines, Iron Age, pork prohibition, Grigson 1982, Lidar SapirHen, applied statistics, Microsoft Excel statistics, binomial distribution, chi square distribution, hypothesis tests, archaeology statistics Author affiliation: Professor Emeritus, Sonoma State University, Rohnert Park, CA, 94928 [email protected], ssrn.com/author=2220942 2 Leviticus 11:7 and Deuteronomy 14:8

ORIGNS OF THE PROHIBITION OF PORK CONSUMPTION, JAMAL MUNSHI, 2014

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2. THEORY

In her study of pig husbandry in Neolithic England, Caroline Grigson used a set of statistical heuristics that have become standardized in this field of research (Grigson, 1982) (Sapir-Hen, 2013). The Grigson guidelines are as follows: 1. The sample size must be greater than 70. 2. The samples are classified into three categories according to pig frequency. 3. Samples with less than 2% pig are classified as pig husbandry low or absent. 4. Samples with 2% to 7% pig are classified as showing moderate levels of pig husbandry. 5. Samples with pig frequency greater than 7% are classified as a high level of pig husbandry. These guidelines do not adequately address the uncertainty in making inferences about ancient farm animals3 from archaeological samples of their remains. For example sample sizes greater than 70 would include for example a sample size of say 7000 and yet the pig content in these two samples would contain very different information because of differences in sampling variation. Sampling variation also implies for example that even a 90% confidence interval4 of the percent pig in an ancient farm that generated a sample data of 3 pig items in a sample of 70 specimens ranges from 0.3% to 8.27% - a range that covers all three pig frequency classifications. Modern studies of pig husbandry involve complex and inter-related hypotheses about the state of ancient animal husbandry. For example, in the extensive study of pig husbandry in the Levant by SapirHen et al (Sapir-Hen, 2013) an elaborate set of hypotheses of differences in pig husbandry among geographical locations and across different eras of the Iron Age are tested using pig frequencies in 76 samples taken from 6 strata and 33 sites. The sample sizes range from 78 to 11,275 specimens. The important finding of the Sapir-Hen study is that pork prohibition was used by the Biblical authors not to divide people as previously thought, but to unite people, specifically, to unite the Judahites and the Northern Israelites rather than to draw an ethnic boundary between them and the Philistines. Among the hypotheses with regard to pig consumption used to support this insight are the following: 3 4

We use this commonly understood phrase to refer to domesticated eaten fauna. A 95% confidence interval would be much larger


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1. Iron Age I and Iron Age IIA&B: Pig consumption (PC) is higher in Philistia than in (Judah-Israel). 2. Iron Age II B&C: PC in Philistia is less in Iron Age IIC than in Iron Age IIB. 3. Late Bronze Age, Iron Age I and Iron Age II: PC is lower in highlands than in lowlands. 4. Iron Age IIA5 and Iron Age IIB: PC in lowlands is higher in the North than in the South6. 5. Relationship #4 is stronger in Iron Age IIB than in Iron Age IIA. 6. Iron Age IIB: rapid increase of PC in the North (compare with Iron Age IIA). 7. Pig avoidance in lowlands and highlands of Judah in Iron Age IIA and Iron Age IIB (less than before). 8. Pig avoidance in the highlands in Iron Age I (compare with lowlands) 9. PC is lower in Iron Age IIC than in Iron Age IIB for all sites. In this note, we formulate these nine findings as research questions, transform the research questions into testable hypotheses, and use the dataset provided by Sapir-Hen et all to carry out the hypothesis tests. In so doing we take note of two unusual characteristics of the data. First, the sample sizes vary by orders of magnitude and that therefore pig fractions cannot be compared directly but must be weighted by sample size. Secondly, we note that many of the pig fractions are very small with most of them under 5% and many under 1%. We use the Chi-Square distribution in making the comparisons but because of concerns about low cell counts we verify all results with the binomial distribution in case an “expected” value of the pig count in a cell of the Chi Square table is small. The advantage of the Chi Square procedure is that it is rather intuitive and easy to rationalize. All nine hypotheses may be tested as a comparison of two proportions. In each case, our null hypothesis is that the two samples to be compared were produced by the same underlying population of farm animals and that the observed difference in pig fraction between them can be explained in terms of sampling variation. If the probability that the observed difference could have been produced by sampling variation is less than a specified value α we reject the null hypothesis and conclude that the populations of farm animals that produced these samples must have had different pig fractions. The value of α is the probability of a false positive7 and may be interpreted as our threshold of disbelief. In our analysis we have decided to set the error rate to α=0.001 in view of a study published by the National Academy of Sciences that the unacceptably high rate of contradictory findings and irreproducible results in research is due to the use of higher α values (Johnson, 2013). A further consideration is the adjustment of α for multiple comparisons (McDonald, 2014) to control for the probability of spurious findings in random data by virtue of repetition. In our case, there are ten comparisons8 to be made and so to hold our experiment-wide error rate to α=0.001 we will make each comparison at the comparison error rate of α=0.001/10 or α=0.0001.

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For the purpose of our analysis we assume that the data for Iron Age IIA is a combination of the data provided for early Iron Age IIA and late Iron Age IIA. 6 We use “North” and “South” to indicate the geographical areas normally associated with the Northern Kingdom of Israel and the Southern Kingdom of Judah respectively. 7 spurious finding of an effect where there is none 8 Hypothesis #5 requires two comparisons.


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3. DATA ANALYSIS

It is important to note that our hypotheses are not about pig fractions in the samples we found but about pig fractions among farm animal populations that produced the samples we found. We propose to set up these hypothesis tests as follows: 1. First we aggregate all available sample data weighted by sample size into the two categories to be compared. For example in the highland-lowland case (hypothesis #4 in the list) we aggregate all samples from the late Bronze Age, Iron Age I, Early Iron Age IIA, and Late Iron Age IIA classified as either highland or lowland. The result of this weighted aggregation process is a set of two samples to be compared each with a summated sample size and a weighted average pig fraction. 2. We combine the two samples to be compared into one large sample to represent the null hypothesis. The pig fraction in the combined sample serves as our estimate of the pig fraction among farm animals in the null hypothesis world that created the sample. 3. Finally we use the Chi Square and binomial probability distributions9 to compute the probability that the samples to be compared were taken from this null hypothesis world. If the probability that either one of the two samples was taken from the null hypothesis world is less than our comparison α level, we reject the null hypothesis and conclude that the two samples were derived from two different populations of farm animals that contained different pig fractions. The procedure assumes that ancient pig and non-pig farm animals had an equal probability of having their remains found by the archaeologists who contributed data to this study. The aggregation of samples from different sites and strata requires the further assumption that the equality of the probability of being found holds across all sites and strata in the study and was not changed from one site to another or from one stratum to another by differences in excavation and data acquisition

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The CHISQ and BINOMDIST functions in Microsoft Excel is used for this purpose.


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methods. A violation of these assumptions may invalidate the findings of the hypothesis tests presented in this note. 3.1 The Data. The pig fraction data are taken from Figure 1 of the Sapir-Hen paper that continues for four pages from page 4 to page 7 (Sapir-Hen, 2013). These data have been reorganized and reformatted to facilitate the kind of data aggregation and data analysis used for the hypothesis tests. The Microsoft Excel spreadsheets containing the reformatted data and the details of the data analysis presented here are available for download in the data archive for this paper (Munshi, Pig paper archive, 2014). A form of dummy coding10 is used to classify the source of each sample into one of five11 time periods as Bronze Age, Iron Age I, Iron Age IIA, Iron Age IIB, or Iron Age IIC; two classifications of elevation as either Highland or Lowland; and four categories according to geographical and social criteria as Philistia, Judah, Israel, and Foreign12. Each dummy code column contains either “1” meaning “Yes” or “0” meaning “No”. A portion of the data is shown in Figure 1. Figure 1 Screen shot showing data format and dummy coding for time period and location information

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Dummy coding is used for ease of aggregating samples into categories and not for regression. In dummy coding for regression one of the categories is represented by all zeroes (Munshi, Dummy coding, 2010) but that is not the case here. 11 The data presented in the paper makes a distinction between Early Iron Age IIA and Late Iron Age IIA and also between Late Bronze Age IIB and Late Bronze Age III but the findings in the Sapir-Hen paper do not make these distinctions and so our data format includes all Iron Age II and Bronze Age data in these two classifications. 12 identified in the paper as Phoenician or Aramaean “foreign material culture” or located in the Negev.


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We see in Figure 1 for example that row 6 contains a sample of 169 specimens 5 of which are from pigs. The sample was taken at the Aphek site. Looking across the row of zeroes and ones we find ones under Iron IIA, Lowland, and Philistia identifying this sample as one that was taken from the Iron Age IIA stratum and classifying the Aphek site as a lowland site located in Philistia. Across the top in row 1 and columns A through K are the counts of the number of samples that have been tagged with ones in each column. Looking across row 1 we find for example that there are 16 samples from the Bronze Age and only 6 from Iron Age IIC. There are 67 samples from lowlands and 9 from highlands. As for geographical location we find that there are 26 samples from Judah, 29 from Israel, 11 from Philistia, and 10 sites identified as Foreign that did not fit any of the other three geographical criteria perhaps having a Phoenician or Aramean culture, or being located in the Negev. In columns L, M, N, and O of row 1 are checksums to make sure that the total counts of mutually exclusive categories add up to the total number of samples. There are a total of 76 samples in this dataset13. The complete dataset is included in the data archive for this paper (Munshi, Pig paper archive, 2014). This data structure facilitates data selection according to any combination of criteria so that all nine hypotheses may be tested. For example to select all samples from lowland sites regardless of stratum, we would simply multiply columns L and M by column F. Similarly to select all samples from highland sites we would multiply columns L and M by column G. The sample data reside in columns L and M. Column L contains the total number of items and column M contains the number of items derived from pigs. To test hypothesis #1 we need a criterion that uses a combination of site location and stratum specification. For the first group we need to select the Iron Age I, Iron Age IIA, and Iron Age IIB strata from all sites that are in Philistia. Accordingly, we add columns B, C, and D, and multiply the result by column J, and then again by columns L and M to extract the data. For the second group we again add columns B, C, and D this time we multiply the result by the sum of columns H and I. The result is then multiplied by L and M to extract the data for these criteria. The result of these computations is shown in Figure 2 and available for download as a file called hypothesis1 in the data archive for this paper (Munshi, Pig paper archive, 2014). Figure 2 shows that the dummy coding for aggregating the two groups of samples are computed and placed in columns P and Q and these codes are used to collect the data from columns L and M and move them to columns R and T for Philistia and to columns S and U for Judah or Israel. Row 1 of these columns contains the sum of these values. They show that for the three strata selected, the total aggregated sample size for Philistia is 2724 of which 21514 are from pigs and the total aggregated sample for Judah

13

Two of the 78 samples listed in the source paper contain no data. The number of items are rounded to integer values. The percentages provided in the source data did not always yield an integer. 14


and Israel combined is 47285 of which 1834 are from pigs. We are now ready to carry out the test for hypothesis #1. Figure 2 Data aggregation for Hypothesis #1

3.2

Hypothesis test #1.

Situation-1: Philistia in Iron Age I, Iron Age IIA, and Iron Age IIB Situation-2: Judah and Israel in Iron Age I, Iron Age IIA, and Iron Age IIB Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001

Situation-1: n=2724 items consisting of 2509 non-pig items and 215 pig items. Situation-2: n=47285 items consisting of 45451 non-pig items and 1834 pig items.

Expected counts under conditions of H0: φ1 = φ2 Situation-1: 2612 non-pig items and 112 pig items Situation-2: 45347 non-pig items and 1938 pig items

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Chi Square Test: P-value = Probability of observing 215 pig items or more in a sample of 2724 items taken from Situation-1 under conditions of H0 is almost zero and well below our critical error rate of 0.0001. Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 3. Figure 3 Data aggregation and Chi Square test for Hypothesis #1

3.3

Hypothesis test #2.

Situation-1: Philistia in Iron Age IIB Situation-2: Philistia in Iron Age IIC Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001



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Chi Square Test: P-value = Probability of observing 83 pig items or more in a sample of 526 items taken from Situation-1 under conditions of H0 is almost zero and well below our critical error rate of 0.0001. Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 4. Figure 4 Data aggregation and Chi Square test for Hypothesis #2

3.4

Hypothesis test #3.

Situation-1: Lowlands in Late Bronze Age, Iron Age I and Iron Age II Situation-2: Highlands in Late Bronze Age, Iron Age I and Iron Age II Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001


Expected counts under conditions of H0: φ1 = φ2 Situation-1: 84453 non-pig items and 2359 pig items Situation-2: 11731 non-pig items and 328 pig items Chi Square Test: P-value = Probability of observing 19 pig items or less in a sample of 12059 items taken from Situation-2 under conditions of H0 is almost zero and well below our critical error rate of 0.0001.

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Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 5. Figure 5 Data aggregation and Chi Square test for Hypothesis #3

3.5

Hypothesis test #4.

Situation-1: Northern lowlands in Iron Age IIA and Iron Age IIB Situation-2: Southern lowlands in Iron Age IIA and Iron Age IIB Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001



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Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 6. Figure 6 Data aggregation ahd Chi Square test for Hypothesis #4

3.6

Hypothesis test #5a.

Situation-1: Northern lowlands in Iron Age IIA Situation-2: Southern lowlands in Iron Age IIA Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001


Expected counts under conditions of H0: φ1 = φ2 Situation-1: 13596 non-pig items and 277 pig items Situation-2: 2268 non-pig items and 46 pig items Chi Square Test: P-value = Probability of observing 8 pig items or less in a sample of 2314 items taken from Situation-2 under conditions of H0 is 8.57x10-10, well below our critical error rate of 0.0001. The result is verified by the binomial distribution probability of 6.4x10-13.

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Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 7. Figure 7 Data aggregation and Chi Square test for Hypothesis #5 Part 1: Iron Age IIA

3.7

Hypothesis test #5b.

Situation-1: Northern lowlands in Iron Age IIB Situation-2: Southern lowlands in Iron Age IIB Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001


Expected counts under conditions of H0: φ1 = φ2 Situation-1: 1338 non-pig items and 20 pig items Situation-2: 6532 non-pig items and 96 pig items Chi Square Test: P-value = Probability of observing 47 pig items or less in a sample of 6628 items taken from Situation-2 under conditions of H0 is 3.2x10-34, well below our critical error rate of 0.0001. The result is verified by the binomial distribution probability of 1.7x10-8.

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Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 8. Figure 8 Data aggregation and Chi Square test for Hypothesis #5: Part 2: Iron Age IIB

The results of hypothesis tests 5a and 5b support the claim that pig consumption in the lowlands of the South was less than pig consumption in the lowlands of the North in both Iron Age IIA and Iron Age IIB. However, if we use the p-value as a measure of the strength of a relationship, we are unable to say whether the relationship was stronger in IIB than in IIA because of conflicting results. The Chi Square pvalue is lower for Iron Age IIB but the p-value computed with the Binomial distribution is lower for Iron Age IIA. The comparison of the strength of the relationship in these two time periods is therefore inconclusive.

3.8

Hypothesis test #6.

Situation-1: Northern Kingdom in Iron Age IIB Situation-2: Northern Kingdom in Iron Age IIA Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001



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Expected counts under conditions of H0: φ1 = φ2 Situation-1: 1324 non-pig items and 34 pig items Situation-2: 13523 non-pig items and 350 pig items Chi Square Test: P-value = Probability of observing 69 pig items or more in a sample of 1358 items taken from Situation-1 under conditions of H0 is almost zero and well below our critical error rate of 0.0001. Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 9. Figure 9 Data aggregation and Chi Square test for Hypothesis #6

3.9

Hypothesis test #7.

Situation-1: Judah in Late Bronze Age and Iron Age I Situation-2: Judah in in Iron Age IIA and Iron Age IIB Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001


Expected counts under conditions of H0: φ1 = φ2 Situation-1: 33634 non-pig items and 1292 pig items


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Situation-2: 12497 non-pig items and 480 pig items Chi Square Test: P-value = Probability of observing 65 pig items or less in a sample of 12977 items taken from Situation-2 under conditions of H0 is almost zero and well below our critical error rate of 0.0001. Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 10. Figure 10 Data aggregation and Chi Square test for Hypothesis #7

3.10

Hypothesis test #8.

Situation-1: Lowlands in Iron Age I Situation-2: Highlands in Iron Age I Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001




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Chi Square Test: P-value = Probability of observing 2 pig items or less in a sample of 2668 items taken from Situation-2 under conditions of H0 is almost zero and well below our critical error rate of 0.0001. Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 11. Figure 11 Data aggregation and Chi Square test for Hypothesis #8

3.11

Hypothesis test #9.

Situation-1: Iron Age IIB Situation-2: Iron Age IIC Research question: Was the pig fraction among farm animals in Situation-1 (φ1) greater than that in Situation-2 (φ2)? H0:

φ1 ≤ φ2

Sample data:

Ha:

φ1 > φ2

α = 0.0001




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Decision: Since the p-value < α, reject H0 and conclude that φ1 > φ2. The sample data provide sufficient evidence that the pig fraction in Situation-1 was greater than the pig fraction in Situation-2. The details of the analysis are included in the data archive for this paper (Munshi, Pig paper archive, 2014). A screen shot of the relevant Excel spreadsheet is shown in Figure 12. Figure 12 Data aggregation and Chi Square test for Hypothesis #9

4. CONCLUSIONS

This note provides strong statistical support for the hypotheses15 about pig fractions of farm animals in the Levant during the Iron Age used by the authors of the Sapir-Hen paper to draw their far reaching conclusions with regard to the motivation for the Biblical prohibition of pork consumption (Sapir-Hen, 2013). Although the authors do not make the data aggregation and the pig fraction comparisons explicit in the paper, preferring instead to refer only to the pig percentage in specific samples, our work shows that the authors have an intimate knowledge of the data and that their intuition about these relationships in time and space are valid because these relationships can be verified by statistical analysis. The further purpose of this note is to propose an alternative format for the presentation of this kind of information. The proposed format is likely to be more suitable for addressing a wider audience.

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With the possible exception of a portion of Hypothesis #5


5. REFERENCES

Draper, N. a. (1981). Applied regression analysis. NY: Wiley. Efron, B. (1993). An introduction to the bootstrap. Chapman. Finkelstein, I. a. (2002). The Bible unearthed. Touchstone. Gerstman. (2006). Proportions. Retrieved 2014, from sjsu.edu: http://www.sjsu.edu/faculty/gerstman/StatPrimer/proportion.pdf Grigson, C. (1982). Porridge and Pannage: Pig Husbandry in Neolithic England. In M. a. Bell, Archaeological Aspects of WoodlandEcology (pp. 297-314). Oxford: Oxford. Johnson, V. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, http://www.pnas.org/content/110/48/19313.full. McCarter, S. (2012). Neolithic. Routledge. McDonald, J. (2014). Handbook of biological statistics. Available online: http://www.biostathandbook.com/multiplecomparisons.html. Mendoza, M. e. (2006). Estimating the body mass of extinct ungulates. Journal of Zoology, http://webpersonal.uma.es/de/ppb/JZool.pdf. Munshi, J. (2010). Dummy coding. Retrieved 2014, from Youtube: http://www.youtube.com/watch?v=UhqCExVWw2Q Munshi, J. (2014). Pig paper archive. Retrieved 2014, from Dropbox: https://www.dropbox.com/sh/6vmqecqkhq6sxcw/AABQ0HdhSrWIv3WQs-9VdIQea?dl=0 Rosenblum, J. (2010). "Why Do You Refuse to Eat Pork?": Jews, Food, and Identity in Roman Palestine. Jewish Quarterly Review, V100 #1 Winter pp 95-110. Sapir-Hen, L. (2013). Pig husbandry in iron age Israel and Judah. ZDPV, 129-1. Schultz, E. (2013). Sedentism. Retrieved 2014, from Primitivism: http://www.primitivism.com/sedentism.htm Simoons, F. (1994). Eat not this flesh. University of Wisconsin Press.

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