# AC 33-13: Sample Size Considerations for Comparative Test and Analysis for Turbine Aircraft Engine and Auxiliary Power Unit (APU), Parts Manufacturer Approval (PMA), and Third-Party Repair Parts

- Type: Advisory circular (guidance, not mandatory unless a rule or AD requires it)
- Status: Current
- Approved: September 16, 2026
- Family: AC 33-13
- Pages: 12
- FAA DRS: https://drs.faa.gov/browse/excelExternalWindow/DRSDOCID147700311620260917122634.0001
- Page: https://openairdocs.org/ac/33-13

## Versions

- AC 33-13 (Current), approved Sep 16, 2026

## Text

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U.S. Department of Transportation Federal Aviation Administration Advisory Circular Subject: Sample Size Considerations for Date: AC No: 33-13 Comparative Test and Analysis for Turbine Initiated By: AIR-625 Change No: N/A Aircraft Engine and Auxiliary Power Unit (APU), Parts Manufacturer Approval (PMA), and Third-Party Repair Parts

**1. PURPOSE.**

1.1 This advisory circular (AC) describes two approaches applicants for a parts manufacturer approval (PMA) may use to define minimum sample sizes for turbine aircraft engine and auxiliary power unit (APU) products and articles when statistical data analysis is used. These approaches support showing compliance with the test reports and computations requirements of title 14, Code of Federal Regulations (14 CFR) § 21.303(a)(4), using the comparative test and analysis approach to demonstrate compliance with 14 CFR part 33 and Technical Standard Order (TSO) C77.

1.2 This guidance supports the development of technical data needed for major repairs (see appendix A to 14 CFR part 43). Parts fabrication and implementation for repairs must comply with § 43.13, which requires that “each person maintaining or altering, or performing preventive maintenance, shall do that work in such a manner and use materials of such a quality, that the condition of the aircraft, airframe, aircraft engine, propeller or appliance worked on will be at least equal to its original or properly altered condition (with regard to aerodynamic function, structural strength, resistance to vibration and deterioration, and other qualities affecting airworthiness).” This AC’s methods support demonstrating that the engine or APU continues to comply with the applicable requirements of part 33 and TSO C77 after maintenance.

**2. APPLICABILITY.**

2.1 The guidance in this AC is directed to applicants requesting Federal Aviation Administration (FAA) approval for PMA when statistical data analysis is used, or repair

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AC 33-13 or alteration of turbine engine and APU parts, FAA engine type certification engineers, and the Administrator’s designees.

2.2 This is a guidance docu ment. Its content is not legally binding in its own right and will not be relied upon by the Department as a separate basis for affirmative enforcement action or other administrative penalty. Conformity with the guidance document is voluntary only. Nonconformity will not affect rights and obligations under existing statutes and regulations.

2.3 This AC is intended only to provide information to the public regarding existing requirements under the law or agency policies. The FAA will consider other methods of demonstrating compliance that an applicant may elect to present. Terms such as “should,” “may,” and “must” are used only in the sense of ensuring applicability of this particular method of compliance when the acceptable method of compliance in this document is used. If the FAA becomes aware of circumstances in which following this AC would not result in compliance with the applicable regulations, the FAA may require additional substantiation as the basis for finding compliance.

2.4 The material in this AC does not change or create any additional regulatory requirements, nor does it authorize changes in, or permit deviations from, existing regulatory requirements.

**3. RELATED READING MATERIAL.**

3.1 Title 14, Code of Federal Regulations.

The following 14 CFR regulations are related to this AC. You can download the full text of these regulations from the Federal Register website at www.ecfr.gov.

• Part 33, Airworthiness Standards: Aircraft Engines.

• Section 21.303, Application.

• Section 43.13, Performance rules (general).

3.2 FAA Publications.

The following ACs are related to the guidance in this AC. The latest version of each AC referenced in this document is available on the FAA website at FAA Advisory Circulars and on the Dynamic Regulatory System.

• AC 20-107B, Composite Aircraft Structure.

• AC 21.303-4, Application for Parts Manufacturer Approval Via Tests And Computations Or Identicality.

• AC 33-8, Guidance for Parts Manufacturer Approval of Turbine Engine and Auxiliary Power Unit Parts under Test and Computation.

• AC 33-9, Developing Data for Major Repairs of Turbine Engine Parts.

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AC 33-13

• AC 33-12, Geometry and Dimensional Considerations for Comparative Test and Analysis for Turbine Engine and Auxiliary Power Unit (APU) Replacement, and Repaired Parts.

• AC 33.83-1, Comparative Method to Show Equivalent Vibratory Stresses and High Cycle Fatigue Capability for Parts Manufacturer Approval of Turbine Engine and Auxiliary Po wer Unit Parts.

• AC 33.87-2, Comparative Endurance Test Method to Show Durability for Parts Manufacturer Approval of Turbine Engine and Auxiliary Power Unit Parts.

• AC 43-18, Fabrication of Aircraft Parts by Maintenance Personnel.

3.3 FAA Orders.

The following orders are related to the guidance in this AC. The latest version of each order referenced in this document is available on the Dynamic Regulatory System.

• Order 8110.42, Parts Manufacturer Approval Procedures.

• Order 8300.16, Major Repair and Alteration Data Approval.

• TSO-C77, Gas Turbine Auxiliary Power Units.

3.4 Industry Publications.

International Organization for Standardization. (1976). ISO 2854:1976, Statistical interpretation of data — Techniques of estimation and tests relating to means and variances. ISO Central Secretariat.

**4. BACKGROUND.**

The FAA has observed that PMA applicants use a variety of statistical principles and methods in their comparative test and analyses to show the proposed PMA part meets airworthiness standards by substantiating that it is at least equal to the original article approved under a type certificate in a particular area or attribute. However, the data often provided is based on statistical methods that are designed to detect statistical differences, not to show equivalence. The sample sizes submitted to the FAA as part of the application often have not provided an adequate demonstration of comparability between the proposed PMA or repair part population and the approved part population, leading to the need for additional guidance on a project-by-project basis. Note that the term “approved part” is used throughout this AC to reference parts approved by type certification, supplemental type certification, or major repair or alteration. Often, proper use of statistical analysis requires larger sample sizes of the comparative populations to demonstrate equivalence.

**5. STATISTICAL PRINCIPLES DISCUSSION.**

5.1 Statistical equivalence tests begin with the null hypothesis, or assumption, that two parts are different by more than an allowed amount (refer to the discussion about parameter d in appendix B.1.1.1 of this AC for more details) and seek evidence to reject that 3

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AC 33-13 hypothesis. The equivalence tests are in contrast to the more common question “are these two populations significantly different?” that inherently assumes a baseline of equivalence.

5.2 In the past, some applicants have proposed analyses that used a trad itional two sample t-test for independent samples1 to compare sample means (i.e., averages) from two populations. Tests, such as this traditional t-test, were used to find a difference, beginning with the null hypothesis that two populations are the same, and attempted to show a difference given a specified level of confidence. This type of test puts the burden on finding a difference. Therefore, this approach is not statistically valid to prove that the two different populations are equally compliant to airworthiness standards. Many applicants are only familiar with this common test for differences and assume that if a t-test fails to detect a difference, the two populations must therefore be equivalent. However, a test result of “no statistically significant difference” means the current evidence is not strong enough to demonstrate that the two parts are different;

this is not the same as showing that the two populations are equivalent.

5.3 Using a small number of samples (small sample sizes) in this regard may compromise the statistical method’s ability to identify differences. This situation arises because a small sample size can make it difficult to identify statistical indications that a difference exists. Therefore, if applicants use a small sample size, population differences may be missed. This test would result in an incorrect conclusion that the populations are equivalent.

5.4 A potential consequence of using a small sample size for a statistical hypothesis test for differences is a failure to find a difference when one exists, called beta error (β) or “Type II error.” (1 - β) is the “Power” of finding a given difference. Saying a difference exists when a difference does not exist is called alpha error, (α) or “Type I error.”

(1 - α) is the “Confidence” level in saying a difference exists. In two one-sided tests for equivalence (TOST), the definitions switch because the two populations are initially assumed to be different and the tests provide a method to reject that assumption. In TOST, a Type I error is falsely declaring equivalence when the two populations are significantly different, while a Type II error is failing to declare equivalence when the two populations are not significantly different. The FAA is concerned with Type I errors in this sense for parts being approved under a major repair or under PMA by comparative test and analysis. Appendix B of this AC provides a detailed summary of how to address both Type I and Type II errors with an appropriate sample size for equivalence testing.

5.5 The finding of equivalence requires a different approach to that described in paragraphs

5.1 and 5.2 of this AC. It requires a test to determine, with a given level of confidence, that the two populations are not different by more than an allowed amount established by parameter d. This AC describes two acceptable approaches to define a minimum

1 A traditional t-test that tests for significant differences is here distinguished from two one-sided t-tests (TOST) that are combined to test for equivalence.

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AC 33-13 sample size to be used as part of these methods of compliance, either through a practical method of evaluating approved parts, or through a more analytically rigorous and statistically accurate method of evaluating the approved parts (refer to appendices A and B of this AC, respectively). Typically, there is a considerable difference in the necessary sample size between these two methods.

5.6 The approach to determining a sample size in appendix B of this AC when attempting to show equivalence may be appropriate when the PMA and approved part populations are normally distributed, believed to have the same variance, and have identical sample sizes. Similar approaches with appropriate modifications may be used when these conditions are not met. Evidence for normality and similar variance is required for appropriate use of appendix B of this AC.

5.7 This AC does not provide a comprehensive technical background for each statistical concept involved in showing equivalence. Applicants may consult published statistical reference books or industry standards such as ISO 2854:1976, Statistical Interpretation of Data — Techniques of Estimation and Tests Relating to Means and Variances for related information. Similarly, unique materials such as composites may have additional or unique requirements; applicants may consider available FAA publications (such as AC 20-107B, Composite Aircraft Structure) associated with the part being proposed.

**6. SUGGESTIONS FOR IMPROVING THIS AC.**

If you have suggestions for improving this AC, you may use the Advisory Circular Feedback Form at the end of this AC.

James D. Foltz Aviation Safety Acting Director, Policy and Standards Division, Aircraft Certification Service 5

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AC 33-13 Appendix A

Appendix A. Practical Approach to Define Sample Size A.1 PRACTICAL APPROACH TO DEFINE SAMPLE SIZE.

A.1.1 This appendix describes a practical approach to define acceptable sample sizes for products and articles for turbine aircraft engines and APUs when statistical data analysis is used as comparative analysis under test and computation. See FAA Order 8110.42D, Section 2-8(c) for further information. This approach may allow applicants to use smaller sample sizes than the statistically derived sample size calculations described in appendix B of this AC.

A.1.2 Using the sample size in this appendix starts with the fundamental assumption that both samples come from populations having the same mean. While results from testing this sample size can support that assumption, it does not rigorously demonstrate equivalence as explained in section 5.2 of this AC. For example, passing a two-sample t-test for significant difference in means using this appendix’s sample size only shows there is no reason to overturn the assumption that both samples have the same population mean.

Failing the test using this sample size would indicate there is strong evidence that the assumption is wrong, and it should prompt additional investigation.

A.1.3 The applicant must provide qualitative and/or quantitative rationale for choosing the sample size in this appendix, which may be smaller than the sample size calculated using appendix B of this AC.

**A.2 IDENTIFYING SAMPLES WITHIN POPULATIONS.**

A.2.1 Minimum sample sizes depend on the particular parameter being evaluated. For measurements such as geometric dimensions and material composition and static properties such as elastic modulus and tensile strength, the FAA considers a minimum sample of 10 approved parts from each of three or more different lots to be acceptable.

For example, 30 parts equally distributed over three or more lots are an acceptable minimum sample using this method. The intent is for the applicant to take measurements of multiple lots to observe a true range of population variation. Unique factors may bias any given lot within a single manufacturing run. Including several lots in sampling provides a better evaluation of population variability, thereby providing a more representative description of the approved part. If lot processing information is not available for the approved parts, the applicant should obtain parts from different sources and over a period of time.

A.2.2 Larger sample sizes may provide a more accurate representation of the approved design, as well as the additional benefit of potentially expanding the acceptable tolerances for the proposed part.

A.2.3 Note that the guidance in this AC does not override guidance in other ACs that list a specific sample size or specific method for performing comparative analysis under test A-1

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AC 33-13 Appendix A and computations to use for a particular subject. This AC provides a method of selecting a sample size if no other guidance material exists.

A.2.4 Inability to Meet Measured Tolerances.

A.2.4.1 It may be desirable to increase the number of approved part samples in an effort to increase the acceptable range for a given parameter. These additional samples should be random.

A.2.4.2 In the case of A.2.4.1 of this AC, the additional approved parts would be added to the overall sample. Parts already included in the analysis may not be eliminated, unless either they clearly fail an outlier test (refer to paragraph A.2.6 of this AC for more details on outliers) or an entirely new proposed part sample population is provided. For example, if a new set of manufacturing controls are put in place when measuring proposed parts, replace the previously manufactured parts with these new parts.

A.2.5 Rationale to Limit Extrapolation.

A.2.5.1 Occasionally, part measurements seem to imply a population distribution that can be identified statistically. Even in those cases, it may be important to stay within observed measurements because the true approved part manufacturing tolerance may be “inspected in” to the approved part population. In this situation, the approved part manufacturing process produces a normal distribution, but the tails of the distribution lie outside the acceptable limits for production acceptance. Applicants should not extrapolate beyond the observed part population when tolerance-based truncation occurs; acceptance decisions must be based only on measured parts.

A.2.5.2 As shown in figure A-1 of this AC, data analysis may imply a normal distribution closely about the mean, but the tails of the distribution are actually truncated. Truncated tails can make the process appear more capable than it is; statistical tolerance intervals must not be extended to implied (unobserved) tails without supporting evidence. In addition, any attempt to identify statistical tolerance limits, as opposed to using measured tolerance limits, will likely result in a proposed part that is not within demonstrated experience, because it is not within the approved part tolerance.

A.2.5.3 If data are not normal, apply a valid transformation. If a transformation is not suitable, analyze using a distribution that best represents the data (e.g., lognormal, Weibull, etc.).

A-2

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AC 33-13 Appendix A Figure A-1. Histogram of Parameter Measurements by Number of Parts A.2.6 Outliers.

A.2.6.1 Applicants should examine the inspection re sults to ensure the data are representative of the sample populations. If either an approved or a proposed part could be an outlier, it may be identified using statistical tests. A typical outlier test involves determining the probability of an extreme measurement existing within the measured sample size. The statistical test may only be used once on any given sample, or data set, meaning that applicants may only eliminate the data point(s) identified by the initial outlier test. Do not eliminate the initial outlier(s) and then perform the statistical outlier test again for the remaining data points.

A.2.6.2 The reason(s) for any pote ntial outlier must be investigated and explained before eliminating the data point(s). Applicants should retest outliers attributed to a faulty test procedure, preferably by use of an improved test procedure. Applicants must not eliminate extreme parts from the statistical population without proof that the part did not conform to design intent.

A.2.6.3 An extreme part that is not attributable to an identifiable cause, and was produced by the proposed production process, may be an indication of an unacceptable production process or part drawing. Such an outlier may require either improvement in the production process or changes to the proposed part design.

A.2.6.4 Often, the resulting data, including what may seem to be unusual data, reflects the true variation in the proposed part design or production process. Therefore, applicants may not remove potential outliers from the analysis without appropriate justification. This A-3

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AC 33-13 Appendix A justification must be in the FAA-approved certification test plan, and include, at a minimum, the following:

• A statistical test;

• A histogram of all the parts to document the outlier’s status visually; and

• Explanations of factors unique to the outlier that provide a basis for not including the part in the rest of the population.

A-4

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AC 33-13 Appendix B Appendix B. Statistical Method for Determining Sample Size B.1 STATIS

**TICALLY ACCURATE METHOD FOR DETERMINING SAMPLE SIZE.**

This appendix describes an alternative approach to appendix A of this AC to define acceptable sample sizes for products and articles for turbine aircraft engines and APUs when statistical data analysis is used as comparative analysis under test and computation. See FAA Order 8110.42D, Section 2-8(c) for further information. This appendix provides an acceptable method for determining the sample size that is necessary to show two sample populations are statistically equivalent within an allowed amount. When applicants start with an agreed hypothesis, namely that the two sample populations are different by at least as much as the allowed amount, they can use a statistically meaningful sample size towards showing a proposed part is sufficiently equivalent to an approved part. Before performing the statistical test, a sample size must be determined.

B.1.1 Calculating Sample Size.

B.1.1.1 Overview of Sample Size and Allowed Difference.

The following formula used within a framework of two one-sided t-tests (TOST) assumes that the standard deviation is known and equivalent between the two populations. The sample size for both proposed and approved parts, N, required to show equivalence within an allowed difference d given the probability of making a Type I or Type II error with population standard deviation, σ, is:

N = [(Z 2 1-α + Z1-β/2) * 2 * σ2] / d2 The Type I (alpha) error is defined here as falsely declaring equivalence when the true difference is greater than d. The Type II (beta) error is defined as failing to declare equivalence even though the two population means are equivalent. The allowed difference, d, is an important parameter in calculating the sample size and is based on experience for what is an acceptable range for a given property of interest. For example, a proposed definition may be “within 3 KSI,” or “within 0.0005 inch tolerance,” or other relevant parameter. Discussions with the FAA are strongly recommended to reach consensus on the proposed allowed difference.

In the formula above, Z1-α is the critical value for the significance level α, and Z1-β/2 is the critical value from the standard normal distribution such that the area under the standard normal curve to the left is (1-β/2). The value Z1-β/2 instead of Zβ is intended to account for symmetric risk. The values for Z1-α and Z1-β/2 are obtained from a standard normal table or may be calculated in Excel via the NORM.S.INV function.

NORM.S.INV returns the inverse of the standard normal cumulative distribution for a probability corresponding to the normal distribution. The input to NORM.S.INV is (1 - β/2) or (1 - α). Values of 0.05 and 0.2 are acceptable for α and β respectively.

B-1

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AC 33-13 Appendix B B.1.1.2 Example Calculation.

For a typical α of 0.05 (5% significance and a (1-2α) or 90% confidence interval) with β of 0.20 (80% power), the z values are 1.645 and 1.282, respectively. N should be rounded up to the next integer.

For α=0.05, β=0.20, σ=8, and d=5, we get:

N = [(1.645 + 1.282)2 2* 2 * (8) ] / (5)2 = 43.9 rounded up to 44 Note 1: N is the required number of both proposed and approved part samples, so the total number of parts for both combined is 88, 44 of each the approved and the proposed parts.

Note 2: As an alternative to the equation in the preceding sections which is considered a close approximation, it is also acceptable to use a sample size determined with open source, industry standard software such as PowerTOST to account for effects not considered in this approximation.

B.1.2 Identifying samples within populations.

As discussed in appendix A, paragraph A.2.1 of this AC, testing parts from more than one lot, melt, or other relevant production factors helps establish the variation introduced by these factors. The intent is for the applicant to take measurements of multiple lots to observe a true range of population variation. Unique factors may bias any given lot within a single manufacturing run. Including several lots in sampling provides a better evaluation of population variability, thereby providing a more representative description of the approved part. Therefore, the sample size, N, determined using the approach in section B.1.1 of this AC should be divided among at least three lots. If lot processing information is not available for the approved parts, the applicant should obtain parts from different sources and over a period of time.

**B.2 ASSUMPTION OF NORMALLY DISTRIBUTED DATA.**

The possibility of a non-normal distribution and truncation of approved part data should be considered and evaluated per the guidance in paragraph A.2.5 of this AC.

**B.3 OUTLIERS.**

Applicants must apply the outlier principles as defined in paragraph A.2.6 of this AC when using appendix B of this AC to determine sample population size.

B-2

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AC 33-13 Appendix B Advisory Ci rcular Feedback Form OMB Control Number: 2120-0746 Expiration Date: 12/31/2027 Paperwork Reduction Act Burden Statement: A federal agency may not conduct or sponsor, and a person is not required to respond to, nor shall a person be subject to a penalty for failure to comply with a collection of information subject to the requirements of the Paperwork Reduction Act unless that collection of information displays a currently valid OMB Control Number. The OMB Control Number for this information collection is 2120-0746. Public reporting for this collection of information is estimated to be approximately 20 minutes per response, including the time for reviewing instructions, searching existing data sources, gathering, and maintaining the data needed, completing, and reviewing the collection of information.

All responses to this collection of information are voluntary. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to: Information Collection Clearance Officer, Federal Aviation Administration, 10101 Hillwood Parkway, Fort Worth, TX 76177-1524.

If you find an error in this Advisory Circular, have recommendations for improving it, or have suggestions for new items/subjects to be added, you may let us know by emailing this form to 9- avs-air-directives-management-officer@faa.gov.

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**FAA 1320-73 (01/25) SUPERSEDES PREVIOUS EDITIONS**

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