Private Hypothesis Testing for Social Sciences
While running any experiment, we often have to consider the statistical power to ensure an effective study. Statistical power or power ensures that we can observe an effect with high probability if such a true effect exists. However, several studies lack the appropriate planning for determining the...
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creator | Mulay, Ajinkya K Lane, Sean Hennes, Erin |
description | While running any experiment, we often have to consider the statistical power
to ensure an effective study. Statistical power or power ensures that we can
observe an effect with high probability if such a true effect exists. However,
several studies lack the appropriate planning for determining the optimal
sample size to ensure adequate power. Thus, careful planning ensures that the
power remains high even under high measurement errors while keeping the type 1
error constrained. We study the impact of differential privacy on experiments
and theoretically analyze the change in sample size required due to the
Gaussian mechanisms. Further, we provide an empirical method to improve the
accuracy of private statistics with simple bootstrapping. |
doi_str_mv | 10.48550/arxiv.2205.05522 |
format | Article |
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to ensure an effective study. Statistical power or power ensures that we can
observe an effect with high probability if such a true effect exists. However,
several studies lack the appropriate planning for determining the optimal
sample size to ensure adequate power. Thus, careful planning ensures that the
power remains high even under high measurement errors while keeping the type 1
error constrained. We study the impact of differential privacy on experiments
and theoretically analyze the change in sample size required due to the
Gaussian mechanisms. Further, we provide an empirical method to improve the
accuracy of private statistics with simple bootstrapping.</description><identifier>DOI: 10.48550/arxiv.2205.05522</identifier><language>eng</language><subject>Computer Science - Cryptography and Security ; Statistics - Methodology</subject><creationdate>2022-05</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,778,883</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2205.05522$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2205.05522$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Mulay, Ajinkya K</creatorcontrib><creatorcontrib>Lane, Sean</creatorcontrib><creatorcontrib>Hennes, Erin</creatorcontrib><title>Private Hypothesis Testing for Social Sciences</title><description>While running any experiment, we often have to consider the statistical power
to ensure an effective study. Statistical power or power ensures that we can
observe an effect with high probability if such a true effect exists. However,
several studies lack the appropriate planning for determining the optimal
sample size to ensure adequate power. Thus, careful planning ensures that the
power remains high even under high measurement errors while keeping the type 1
error constrained. We study the impact of differential privacy on experiments
and theoretically analyze the change in sample size required due to the
Gaussian mechanisms. Further, we provide an empirical method to improve the
accuracy of private statistics with simple bootstrapping.</description><subject>Computer Science - Cryptography and Security</subject><subject>Statistics - Methodology</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzsFugzAQBFBfeqjSfkBP9Q9AbWOvwzGK2iYSUivBHa1haS0RiGyEyt8nIT2MZk6jx9iLFKneGiPeMPz5OVVKmFQYo9QjS7-Dn3EifljO4_RL0UdeUZz88MO7MfBybDz2vGw8DQ3FJ_bQYR_p-b83rPp4r_aHpPj6PO53RYJgVaJzI5yzLcpWba26LuHQSWkQAUSmLWSgW9BghdWgJYHJNcE1rqMcXbZhr_fbFVyfgz9hWOobvF7h2QVndzxt</recordid><startdate>20220507</startdate><enddate>20220507</enddate><creator>Mulay, Ajinkya K</creator><creator>Lane, Sean</creator><creator>Hennes, Erin</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20220507</creationdate><title>Private Hypothesis Testing for Social Sciences</title><author>Mulay, Ajinkya K ; Lane, Sean ; Hennes, Erin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a672-4950bb7da1d2872b7d0bab115aa6603476364d6467074641e6594e694ebfe9ab3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Cryptography and Security</topic><topic>Statistics - Methodology</topic><toplevel>online_resources</toplevel><creatorcontrib>Mulay, Ajinkya K</creatorcontrib><creatorcontrib>Lane, Sean</creatorcontrib><creatorcontrib>Hennes, Erin</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mulay, Ajinkya K</au><au>Lane, Sean</au><au>Hennes, Erin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Private Hypothesis Testing for Social Sciences</atitle><date>2022-05-07</date><risdate>2022</risdate><abstract>While running any experiment, we often have to consider the statistical power
to ensure an effective study. Statistical power or power ensures that we can
observe an effect with high probability if such a true effect exists. However,
several studies lack the appropriate planning for determining the optimal
sample size to ensure adequate power. Thus, careful planning ensures that the
power remains high even under high measurement errors while keeping the type 1
error constrained. We study the impact of differential privacy on experiments
and theoretically analyze the change in sample size required due to the
Gaussian mechanisms. Further, we provide an empirical method to improve the
accuracy of private statistics with simple bootstrapping.</abstract><doi>10.48550/arxiv.2205.05522</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Cryptography and Security Statistics - Methodology |
title | Private Hypothesis Testing for Social Sciences |
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