{"id":46052,"date":"2023-03-29T10:24:55","date_gmt":"2023-03-29T09:24:55","guid":{"rendered":"https:\/\/www.idsurvey.com\/?page_id=46052"},"modified":"2024-02-15T10:21:15","modified_gmt":"2024-02-15T09:21:15","slug":"ab-testing-calculator","status":"publish","type":"page","link":"https:\/\/www.idsurvey.com\/en\/ab-testing-calculator\/","title":{"rendered":"A\/B testing calculator"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"46052\" class=\"elementor elementor-46052\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a87a8e7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a87a8e7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-wide\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cbfd0c1\" data-id=\"cbfd0c1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7770f62 elementor-widget elementor-widget-heading\" data-id=\"7770f62\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">AB testing calculator<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-38ae938 elementor-widget elementor-widget-heading\" data-id=\"38ae938\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">A\/B testing for statistical significance calculation.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-70be999 elementor-widget elementor-widget-html\" data-id=\"70be999\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n    .main-container{\n        font-size:14px;\n        gap: 40px;\n    }\n    .left-container{\n        border-top: 5px solid #e0e0e0;\n        background:#F9F9F9;\n        padding:10px 20px;\n        border-radius:5px;}\n    .dflex{display:flex;}\n    .space-between{justify-content:space-between;}\n    .gap5{gap:5px;}\n    .gap30{gap:30px;}\n    .flex-col{flex-direction:column;}\n    .flex1{flex: 1 1 auto;}\n    .fbasis1{flex-basis:33%}\n    .fbasis2{flex-basis:66%}\n    .fbasis45{flex-basis:45%;}\n    .miniTitlefont{font-size: 14px;}\n    .miniTitlefont > span {font-weight: bold;}\n    .minifont{font-size: 10px;}\n    .smallfont{font-size: 18px;}\n    .middlefont{font-size:25px;}\n    .bigfont{font-size:30px;}\n    input[type=\"radio\"]{-webkit-appearance: auto;width:20px;height:20px}\n    .spacer5{margin-top:5px;}\n    .spacer10{margin-top:10px;}\n    .spacer15{margin-top:15px;}\n    .spacer20{margin-top:20px;}\n    .spacer30{margin-top:30px;}\n    .border-bottom{border-bottom: 1px solid #ddd;}\n    input {max-width:100% !important;font-size:18px;border: 1px solid #959595 !important;}\n    .ab-send-btn{\n    border: none;\n    padding: 10px;\n    margin-bottom: 20px;\n    margin-top: 20px;\n    color: #FFF !important;\n    display: block;\n    border-radius: 5px;\n    font-weight: 400;\n    font-size: 20px;\n    background: #44AD45;\n    width:100%;\n    text-align: center;\n}\n\n.pre-st{font-size:16px;font-family:\"San Francisco\", Sans-serif;}\n\n.radioBtn{\n    display:flex;\n    align-items: center;\n    gap: 5px;\n}\n.radioBtn *{\n    cursor: pointer;\n}\n.radioBtn + .radioBtn{\n    margin-top: 5px;\n}\n\n.tooltip {\n    padding:5px;\n    cursor: default;\n    position: relative;\n    display: inline-block;\n}\n\n.tooltip .tooltiptext {\n  visibility: hidden;\n  width: 200px;\n  background-color: #fff;\n  color: #222;\n  border-radius: 5px;\n  padding: 5px 0;\n  position: absolute;\n  z-index: 1;\n  padding: 10px;\n  top: 150%;\n  left: 50%;\n  margin-left: -100px;\n  font-size:13px;\n  line-height: 1.25;\n  box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.2);\n}\n\n.tooltip .tooltiptext::after {\n  content: \"\";\n  position: absolute;\n  bottom: 100%;\n  left: 50%;\n  margin-left: -5px;\n  border-width: 5px;\n  border-style: solid;\n  border-color:  transparent transparent #fff transparent;\n}\n\n.tooltip:hover .tooltiptext {\n      visibility: visible;\n}\n\npre{\n    display: block;\n    padding: 9.5px;\n    margin: 5px 0 10px;\n    font-size: 13px;\n    line-height: 1.42857143;\n    color: #333;\n    word-break: break-all;\n    word-wrap: break-word;\n    background-color: #f5f5f5;\n    border: 1px solid #ccc;\n    border-radius: 4px;\n    \n}\n\n.boxResult{\n    padding: 20px;\n    margin-bottom: 20px;\n    overflow: auto;\n    border: 1px solid #eee;\n    border-top-width: 5px;\n    border-radius: 3px;\n}\n\n.boxSuccess{\n    border-top-color: #5cb85c;\n    background-color: #eefcee;\n}\n\n.boxError{\n    border-top-color: #0c6bb5;\n    background-color: #ebf6fe;\n}\n\n.boxAlert{\n    border-top-color: #f06f30;\n    background-color: #FDF2ED;\n}\n\ninput + label{\n    width: 100%;\n    padding: 3px 0;\n}\n\n@media(max-width:1024px){\n    .main-container,\n    .dataContainer{\n        flex-direction: column;\n    }\n    \n    .dataContainer{\n        gap: 10px;\n    }\n    \n    .hideOnMobile{\n        display:none;\n    }\n    \n    .miniTitlefont{\n        font-size: 14px;\n    }\n    \n    .minifont{\n        font-size: 12px;\n    }\n}\n\n<\/style>\n\n<script src=\"\/js\/jStat.js\"><\/script>\n\n<div class=\"dflex main-container\">\n    <div class=\"fbasis1 left-container\">\n        <div class=\"middlefont\">\n            <b>Is your A\/B testing statistically significant?<\/b>\n        <\/div>\n        <div class=\"\"><i style=\"color:#808080;\">\n            Experiment with the settings and gain a deeper understanding of how decreasing the confidence level can enhance the effect size or how increasing the sample size can render a minor difference in the control and experimental groups statistically significant!<\/i>\n        <\/div>\n        <div class=\"smallfont spacer20\">\n            <b>Pre-test or Post-test?<\/b>\n        <\/div>\n        <div>\n            <div class=\" spacer10 radioBtn\">\n                <input type=\"radio\" name=\"testtype\" value=\"pre\" id=\"testtypePre\"><label for=\"testtypePre\"> Pre-test calculation<\/label>\n            <\/div>\n            <div class=\" radioBtn\">\n                <input type=\"radio\" name=\"testtype\" id=\"testtypeEva\" value=\"post\" checked=\"checked\"><label for=\"testtypeEva\"> Post-test evaluation<\/label>\n            <\/div>\n        <\/div>\n        <div class=\"smallfont spacer20 border-bottom\">\n            <b>Data<\/b>\n        <\/div>\n        <div id=\"pretest\" style=\"display: none;\">\n\n            <div class=\"dflex spacer10 space-between flex-col\">\n              <div class=\"form-group\">\n                <div>Unique visitors expected per variation<\/div>\n                <div><input id=\"ptUsers\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" class=\"form-control input-lg\"><\/div>\n              <\/div>\n              <div class=\"form-group spacer10 \">\n                <div>Number of expected conversions Control<\/div>\n                <div><input id=\"ptConversions\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" class=\"form-control input-lg\"><\/div>\n              <\/div>\n              <div class=\"form-group spacer10\">\n                <div>Expected uplift (%)<\/div>\n                <div><input id=\"ptUplift\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" class=\"form-control input-lg\"><\/div>\n              <\/div>\n\n            <\/div>\n\n          <\/div>\n        <div class=\"\" id=\"posttest\">\n        \t<div class=\"dflex spacer10 space-between\">\n                <div class=\"fbasis45\">\n                \t<div>A - Visitors<\/div>\n                    <div><input id=\"usersA\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" value=\"50000\"><\/div>\n                <\/div>\n                <div class=\"fbasis45\">\n                     <div>A - Conversions<\/div>\n                     <div><input id=\"conversionsA\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" value=\"500\"><\/div>\n            \t<\/div>\n            <\/div>\n            <div class=\"dflex spacer10 space-between\">\n                <div class=\"fbasis45\">\n                \t<div>B - Visitors<\/div>\n                    <div><input id=\"usersB\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" value=\"50000\"><\/div>\n                <\/div>\n                <div class=\"fbasis45\">\n                     <div>B - Conversions<\/div>\n                     <div><input id=\"conversionsB\" inputmode=\"numeric\" pattern=\"[0-9]*\" type=\"text\" value=\"570\"><\/div>\n            \t<\/div>\n            <\/div>\n        <\/div>\n        <div>\n        <div>\n            <a href=\"#\" id=\"calculate\" class=\"ab-send-btn\">Calculate<\/a>\n        <\/div>\n        <\/div>\n         <div class=\"smallfont spacer20 border-bottom\">\n            <b>Settings<\/b>\n        <\/div>\n        <div class=\"smallfont spacer10\">\n            Hypothesis \n            <sup class=\"tooltip\">\n                ( i )\n                <div class=\"tooltiptext\">\n                Do you wish to be confident whether the conversion rate of B is lower? If you choose 1-sided then no conclusive statement can be made if the conversion rate of B is lower than of A.\n                <\/div>\n                <\/sup>\n        <\/div>\n        <div class=\" spacer10 radioBtn\">\n            <input type=\"radio\" name=\"hypothesis\" id=\"hypothesis1\" value=\"1\" >\n            <label for=\"hypothesis1\">One-sided<\/label>\n        <\/div>\n        <div class=\" spacer10 radioBtn\">\n            <input type=\"radio\" name=\"hypothesis\" id=\"hypothesis2\" value=\"2\" checked=\"checked\"> <label for=\"hypothesis2\">Two-sided<\/label>\n        <\/div>\n        <div class=\"smallfont spacer20\">\n            Confidence \n             <sup class=\"tooltip\">\n                 ( i )\n             <div class=\"tooltiptext\">The level of confidence you can have that your results are not due to random chance.\n             <\/div>\n             <\/sup>\n        <\/div>\n        <div class=\"spacer10 radioBtn\">\n            <input type=\"radio\" name=\"confidence\" id=\"confidence1\" value=\"0.9\"> <label for=\"confidence1\">90%<\/label>\n        <\/div>\n        <div class=\"spacer10 radioBtn\">\n            <input type=\"radio\" name=\"confidence\" id=\"confidence2\" value=\"0.95\" checked=\"checked\"> <label for=\"confidence2\">95%<\/label>\n        <\/div>\n        <div class=\"spacer10 radioBtn\">\n            <input type=\"radio\" name=\"confidence\" id=\"confidence3\" value=\"0.99\"> <label for=\"confidence3\">99%<\/label>\n        <\/div>\n    <\/div>\n    <div class=\"fbasis2\">\n        <div class=\"boxResult\">\n            <div class=\"middlefont\">\n                <b>\n                <span id=\"result_txt\">The test result is <em>not<\/em> significant.<\/span>\n                <\/b>\n            <\/div>\n            <div class=\"smallfont\">\n                    <span id=\"result_is_significant\">\n                    Variation B showed a conversion rate of (<span id=\"resCrB\">2.06%<\/span>), which was <span id=\"resUplift\">2.55%<\/span> <span id=\"higherlower\">higher<\/span> than the conversion rate of Variation A (<span id=\"resCrA\">2.01%<\/span>). With a <span id=\"result_conf\">95<\/span>% level of confidence, it can be concluded that this outcome was a direct consequence of the modifications made and not merely due to chance.\n                    <\/span> \n                    <span id=\"result_isnot_significant\" style=\"display: none;\">The observed difference in conversion rate (<span id=\"resUplift2\">2.55%<\/span>) isn't big enough to declare a significant winner. There is no real difference in performance between A and B or you need to collect more data.<\/span>\n            <\/div>\n        <\/div>\n        \n        <div id=\"srm_info\" class=\"boxResult boxAlert\">\n            <div class=\"middlefont\">\n                <b><span id=\"result_txt\">Possible SRM alert<\/span><\/b>\n            <\/div>\n            <div class=\"smallfont\">\n\n                <span>Assuming you intented to have a 50% \/ 50% split, a Sample Ratio Mismatch (SRM) check indicates there might be a problem with your distribution.<\/span>\n            <\/div>\n\n            \n          <\/div>\n        \n        <div class=\"dflex gap30 border-bottom spacer30 dataContainer\">\n            \n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Conversion Rate Control<\/span>\n              <div class=\"minifont\">Conversions A \/ Visitors A<\/div>\n              <pre class=\"pre-st\" id=\"crA\"><\/pre>\n            <\/div>\n\n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Conversion Rate B<\/span>\n              <div class=\"minifont\">Conversions B \/ Visitors B<\/div>\n              <pre class=\"pre-st\" id=\"crB\"><\/pre>\n            <\/div>\n\n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Relative uplift in Conv. Rate<\/span>\n              <div class=\"minifont\">CR<sub>B<\/sub> - CR<sub>A<\/sub> \/ CR<sub>A<\/sub><\/div>\n              <pre class=\"pre-st\" id=\"crUplift\" class=\"\"><\/pre>\n            <\/div>\n\n            \n        <\/div>\n        <div class=\"dflex gap30 spacer30 dataContainer\">\n            \n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Observed Power<\/span>\n                <div class=\"minifont hideOnMobile\">&nbsp;<\/div>\n              <pre class=\"pre-st\" id=\"power\"><\/pre>\n            <\/div>\n\n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>p value<\/span>\n                <div class=\"minifont hideOnMobile\">&nbsp;<\/div>\n              <pre class=\"pre-st\" id=\"pValue\"><\/pre>\n            <\/div>\n        \n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Z-score<\/span>\n                <div class=\"minifont\">( CR<sub>B<\/sub> - CR<sub>A<\/sub> ) \/ SE<sub>difference<\/sub><\/div>\n              <pre class=\"pre-st\" id=\"zScore\"><\/pre>\n            <\/div>\n\n            \n        <\/div>\n        <div class=\"dflex gap30  spacer30 dataContainer\">\n\n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Standard error A<\/span>\n              <div class=\"minifont\">( CR<sub>A<\/sub> * (1-CR<sub>A<\/sub> ) \/ Visitors<sub>A<\/sub>)<sup>1\/2<\/sup><\/div>\n              <pre class=\"pre-st\" id=\"seA\"><\/pre>\n            <\/div>\n\n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Standard error B<\/span>\n                <div class=\"minifont\">( CR<sub>B<\/sub> * (1-CR<sub>B<\/sub> ) \/ Visitors<sub>B<\/sub>)<sup>1\/2<\/sup> <\/div>\n              <pre class=\"pre-st\" id=\"seB\"><\/pre>\n            <\/div>\n\n            <div class=\"miniTitlefont flex1 fbasis1\">\n              <span>Std. Error of difference<\/span>\n                <div class=\"minifont\">SE<sub>difference<\/sub> = ( SE<sub>A<\/sub><sup>2<\/sup> + SE<sub>B<\/sub><sup>2<\/sup> )<sup>1\/2<\/sup><\/div>\n              <pre class=\"pre-st\" id=\"seDiff\"><\/pre>\n            <\/div>\n        <\/div>\n        \n  \n        \n        \n<\/div>\n\n\n\n\n\n\n\n\n\n<script>\n    \n    var cleanseUrl = false;\nvar calcs = 0;\n\nfunction getPathFromUrl(url) {\n            return url.split(\/[?#]\/)[0];\n        }\n\nvar zTable = {\n  1: {\n    '0.9': 1.281551,\n    '0.95': 1.644853,\n    '0.99': 2.326348\n  },\n  2: {\n    '0.9': 1.644853,\n    '0.95': 1.959964,\n    '0.99': 2.575829\n  },\n};\n\nvar txtLib = {\n  'result_ISsignificant': 'Significant result!',\n  'result_NOTsignificant': 'The result is <em>not<\/em> significant.'\n}\n\nfunction normDist(x, mean, sd, cumulative) {\n            \/\/ Check parameters\n            if (isNaN(x) || isNaN(mean) || isNaN(sd))\n                return '#VALUE!';\n            if (sd <= 0)\n                return '#NUM!';\n            \/\/ Return normal distribution computed by jStat [http:\/\/jstat.org]\n            return (cumulative) ? jStat.normal.cdf(x, mean, sd) : jStat.normal.pdf(x, mean, sd);\n        }\n\nfunction perc(number) {\n  return (number * 100).toFixed(2) + '%';\n}\n\nfunction round(num, dec) {\n  return Math.round(num * Math.pow(10, dec)) \/ Math.pow(10, dec);\n}\n\nfunction isNum(n) {\n        return !isNaN(parseFloat(n)) && isFinite(n);\n    }\n\n\/\/Funzione del click su pulsante\n  jQuery('input[type=radio][name=testtype]').on(\"change\", function() {\n    if (this.value == 'pre') {\n      jQuery('#ptUsers').val(jQuery('#usersA').val());\n      jQuery('#ptConversions').val(jQuery('#conversionsA').val());\n      jQuery('#ptUplift').val(Math.round((jQuery('#conversionsB').val() \/ jQuery('#conversionsA').val() - 1) * 100));\n      jQuery(\"#pretest\").show();\n      jQuery(\"#posttest\").hide();\n    } else if (this.value == 'post') {\n      jQuery(\"#posttest\").show();\n      jQuery(\"#pretest\").hide();\n    }\n  });\n\njQuery('#calculate').on('click', function(e) {\ne.preventDefault();\n  if (window.history.replaceState && cleanseUrl) {\n    \/\/prevents browser from storing history with each change:\n    window.history.replaceState({}, null, getPathFromUrl(window.location.href));\n  }\n\n  cleanseUrl = getPathFromUrl(window.location.href);\n\n  if (jQuery(\"input[name=testtype]:checked\").val() == 'pre') {\n    if (!(isNum((jQuery('#ptUsers').val())) && isNum((jQuery('#ptConversions').val())) && isNum((jQuery('#ptUplift').val())))) {\n      alert('please fill all fields with numbers')\n      return false;\n    }\n    jQuery('#usersA').val(jQuery('#ptUsers').val());\n    jQuery('#usersB').val(jQuery('#ptUsers').val());\n    jQuery('#conversionsA').val(jQuery('#ptConversions').val());\n    jQuery('#conversionsB').val(Math.round(parseInt(jQuery('#ptConversions').val()) * (1 + parseInt(jQuery('#ptUplift').val()) \/ 100)));\n  }\n\n  usersA = parseInt(jQuery('#usersA').val());\n  usersB = parseInt(jQuery('#usersB').val());\n  conversionsA = parseInt(jQuery('#conversionsA').val());\n  conversionsB = parseInt(jQuery('#conversionsB').val());\n  tailed = jQuery(\"input[name=hypothesis]:checked\").val();\n  confidence = Number(jQuery(\"input[name=confidence]:checked\").val());\n  crA = conversionsA \/ usersA;\n  crB = conversionsB \/ usersB;\n  crUplift = (crB - crA) \/ crA;\n  seA = Math.sqrt((crA * (1 - crA)) \/ usersA);\n  seB = Math.sqrt((crB * (1 - crB)) \/ usersB);\n  seDiff = Math.sqrt(Math.pow(seA, 2) + Math.pow(seB, 2));\n  zScore = (crB - crA) \/ seDiff;\n  zCritical = zTable[tailed][confidence];\n  lowerA = crA - (zCritical * seA);\n  upperA = crA + (zCritical * seA);\n  lowerB = crB - (zCritical * seB);\n  upperB = crB + (zCritical * seB);\n  powerInput = (crA + seA * zCritical - crB) \/ seB;\n  pValue = 1 - normDist(zScore, 0, 1, true);\n  significant = (pValue < (1 - confidence) && tailed == 1 || ((pValue > (confidence + (1 - confidence) \/ 2) || pValue < (1 - confidence - (1 - confidence) \/ 2)) && tailed == 2)) ? true : false;\n  positive = (crB > crA) ? true : false;\n  if (positive || tailed == 1) {\n    jQuery('#crUplift,.bs-callout').removeClass('negative');\n  } else if (!(positive) && tailed == 2) {\n    jQuery('#crUplift,.bs-callout').addClass('negative');\n  }\n  if (positive || tailed == 1) {\n    power = 1 - normDist(((crA + seA * zCritical - crB) \/ seB), 0, 1, true);\n  } else {\n    power = 1 - normDist(((crB + seB * zCritical - crA) \/ seA), 0, 1, true);\n  }\n\n  jQuery('#crA,#resCrA').html(perc(crA));\n  jQuery('#crB,#resCrB').html(perc(crB));\n  jQuery('#crUplift,#resUplift,#resUplift2').html(perc(crUplift));\n  jQuery('#seA').html(round(seA, 6));\n  jQuery('#seB').html(round(seB, 6));\n  jQuery('#seDiff').html(round(seDiff, 6));\n  jQuery('#zScore').html(round(zScore, 4));\n  jQuery('#zCritical').html(zCritical);\n  jQuery('#lowerA').html(lowerA);\n  jQuery('#power').html(perc(power));\n\n\n  if (tailed == 2 && pValue > 0.5)\n    pValue = 1 - pValue;\n  if (tailed == 2)\n    pValue = pValue * 2;\n  jQuery('#pValue').html((pValue).toFixed(4));\n\n  if (significant == true) {\n      jQuery('.boxResult').removeClass(\"boxError\").addClass(\"boxSuccess\")\n    jQuery('#result_txt').html(txtLib.result_ISsignificant).parent().removeClass('bs-callout-primary').addClass('bs-callout-success');\n\n    jQuery('#result_is_significant').show();\n    jQuery('#result_isnot_significant').hide();\n    jQuery('#result_conf').text(confidence * 100);\n    jQuery('#result_tailed').text(tailed);\n    jQuery('#crUplift').addClass('success');\n    jQuery('#higherlower').html((crA > crB) ? \"lower\" : \"higher\");\n    if (crA > crB)\n      jQuery('#resUplift').html(perc(Math.abs(crUplift)));\n  } else {\n    jQuery('.boxResult').removeClass(\"boxSuccess\").addClass(\"boxError\")\n    jQuery('#result_txt').html(txtLib.result_NOTsignificant).parent().removeClass('bs-callout-success').addClass('bs-callout-primary');\n    jQuery('#result_nrconversion').hide();\n    jQuery('#result_isnot_significant').show();\n    jQuery('#result_is_significant').hide();\n    jQuery('#crUplift').removeClass('success');\n  }\n  var srm_n = parseInt(usersA) + parseInt(usersB);\n  var srm_p = parseInt(usersB) \/ srm_n;\n  var srm_e = parseFloat(0.5);\n  var srm_pvalue = jStat.ztest(srm_p, srm_e, Math.sqrt(srm_p * (1 - srm_p) \/ srm_n));\n  if (srm_pvalue < 0.001) {\n    jQuery('#srm_pvalue').html(srm_pvalue);\n    jQuery('#srm_info').show();\n  } else {\n    jQuery('#srm_info').hide();\n\n  }\n  jQuery('.result_nrboxes').show();\n  jQuery('#shareurl').val(window.location.href.split('?')[0] + '?ua=' + usersA + '&ub=' + usersB + '&ca=' + conversionsA + '&cb=' + conversionsB + ((tailed == 2) ? '&tail=2' : '') + ((confidence == 0.95) ? '' : '&sig=' + confidence * 100) + ((jQuery(\"input[name=testtype]:checked\").val() == 'pre') ? '&pre=1&up=' + jQuery('#ptUplift').val() : '')).hide();\n  jQuery('#geturl, #result_power_container').show();\n\n  calcs++;\n}).trigger(\"click\");\n\n    \n    \n<\/script>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a7e5257 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a7e5257\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-722fbac\" data-id=\"722fbac\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-7cd3955 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7cd3955\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-f787e3f\" data-id=\"f787e3f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4531699 elementor-widget elementor-widget-heading\" data-id=\"4531699\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Discover IdSurvey<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ebd026b elementor-widget elementor-widget-text-editor\" data-id=\"ebd026b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe most powerful survey software, loved by professionals.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e68faa9 elementor-align-center yellow_btn elementor-widget elementor-widget-button\" data-id=\"e68faa9\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.idsurvey.com\/en\/demo-request\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-chevron-right\" viewBox=\"0 0 320 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M285.476 272.971L91.132 467.314c-9.373 9.373-24.569 9.373-33.941 0l-22.667-22.667c-9.357-9.357-9.375-24.522-.04-33.901L188.505 256 34.484 101.255c-9.335-9.379-9.317-24.544.04-33.901l22.667-22.667c9.373-9.373 24.569-9.373 33.941 0L285.475 239.03c9.373 9.372 9.373 24.568.001 33.941z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Request demo<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-50d5d7f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"50d5d7f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4bd499d\" data-id=\"4bd499d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c7e8b9b elementor-widget elementor-widget-heading\" data-id=\"c7e8b9b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">FAQ<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2e9c04a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2e9c04a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-74b46d8\" data-id=\"74b46d8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d6456ab elementor-widget elementor-widget-heading\" data-id=\"d6456ab\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is A\/B testing?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7111cff elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7111cff\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4dea2d7\" data-id=\"4dea2d7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-32e0bfa elementor-widget elementor-widget-text-editor\" data-id=\"32e0bfa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>A\/B testing, also known as split testing, is a method of comparing two versions of a webpage, advertisement, or other marketing asset to determine which version performs better. In an A\/B test, two versions of a webpage are shown to different segments of website visitors at the same time, with one version being the control (original) and the other being the variation (test). The performance of each version is then compared based on specific metrics, such as conversion rates or click-through rates, to determine which version is more effective.<br \/><br \/>For example, an e-commerce website might create two versions of a product page, one with a blue &#8220;Buy Now&#8221; button and the other with a green &#8220;Buy Now&#8221; button. Visitors to the site are randomly assigned to one of the two versions, and their behavior is tracked. If the version with the green button generates more sales, it can be concluded that the green button is more effective at encouraging visitors to make a purchase.<br \/>A\/B testing can be used for a wide range of marketing activities, including email campaigns, landing pages, pricing strategies, and product design. It is an effective way to optimize marketing efforts and improve conversion rates.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1d99a67 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1d99a67\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1c4adac\" data-id=\"1c4adac\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8be5ab1 elementor-widget elementor-widget-heading\" data-id=\"8be5ab1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is statistical significance?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-06e50d8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"06e50d8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-336606f\" data-id=\"336606f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b50e150 elementor-widget elementor-widget-text-editor\" data-id=\"b50e150\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Statistical significance is a term used in statistics to indicate whether an observed difference between two groups or a relationship between two variables is likely to be genuine or simply due to chance. In other words, it measures the likelihood that a particular result is not due to random variation.<\/p><p>Statistical significance is typically determined by calculating a p-value, which is the probability of obtaining a result as extreme or more extreme than the observed result, assuming that the null hypothesis is true. The null hypothesis is the assumption that there is no real difference or relationship between the groups or variables being compared.<br \/><br \/>If the p-value is very low (typically less than 0.05), it is considered statistically significant, meaning that it is unlikely that the observed result is due to chance. If the p-value is higher than 0.05, the result is not considered statistically significant, meaning that it is possible that the observed difference or relationship could be due to chance.<br \/><br \/>It is important to note that statistical significance does not necessarily mean practical significance or importance. A result may be statistically significant but have little practical importance, or vice versa. Additionally, statistical significance does not prove causation, as correlation does not necessarily imply causation.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-31b2295 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"31b2295\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fbfdb90\" data-id=\"fbfdb90\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-13a077b elementor-widget elementor-widget-heading\" data-id=\"13a077b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How to calculate statistical significance with A\/B testing?\n<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bf9d4b2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bf9d4b2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9d0c407\" data-id=\"9d0c407\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2c1fe41 elementor-widget elementor-widget-text-editor\" data-id=\"2c1fe41\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>To calculate statistical significance with A\/B testing, you can use a statistical test such as the two-sample t-test or the z-test. These tests compare the means of two groups (control and variation) and determine whether the difference between them is statistically significant or not.<br \/>Here are the general steps for calculating statistical significance with A\/B testing:<\/p><ol><li>Choose a statistical test: Depending on the size of your sample and the type of data you are analyzing, you can choose between a two-sample t-test or a z-test.<\/li><li>Define your null and alternative hypotheses: The null hypothesis is that there is no significant difference between the control and variation groups, while the alternative hypothesis is that there is a significant difference.<br \/><br \/><\/li><li>Collect data: Collect data from both the control and variation groups for a specific period of time.<br \/><br \/><\/li><li>Calculate the test statistic: Use the chosen statistical test to calculate the test statistic (t-statistic or z-score).<br \/><br \/><\/li><li>Determine the p-value: Calculate the p-value, which is the probability of observing a result as extreme as the one you obtained, assuming the null hypothesis is true.<br \/><br \/><\/li><li>Compare the p-value with the significance level \u03b1. If the p-value is less than \u03b1, one can reject the null hypothesis and conclude that there is a significant difference or relationship between the variables of interest. Otherwise, one cannot reject the null hypothesis and conclude that the observed difference or relationship could be the result of chance or sampling error.<br \/><br \/><\/li><\/ol><p>In general, calculating statistical significance requires basic knowledge of statistics and the use of specialized software tools or calculators, especially for more complex tests or multivariate analyses.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0589bce elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0589bce\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-61ca943\" data-id=\"61ca943\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1ca034f elementor-widget elementor-widget-heading\" data-id=\"1ca034f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is null hypothesis?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0e25508 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0e25508\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f43541a\" data-id=\"f43541a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-de22411 elementor-widget elementor-widget-text-editor\" data-id=\"de22411\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In statistics, the null hypothesis is a statement that assumes there is no significant difference between two groups or variables. It is usually denoted as &#8220;H0.&#8221;<\/p><p>The null hypothesis is used to test whether a particular result is due to chance or if it is statistically significant. For example, if you want to test whether a new drug is effective, the null hypothesis would be that there is no difference in effectiveness between the new drug and the old one.<\/p><p>To test the null hypothesis, researchers will collect data and perform statistical analysis to determine the probability that the observed results are due to chance. If the probability is very low (typically less than 5%), then researchers reject the null hypothesis and conclude that there is a significant difference between the groups or variables being compared.<\/p><p>It is important to note that rejecting the null hypothesis does not necessarily mean that the alternative hypothesis (i.e., the hypothesis being tested) is true. It simply means that there is sufficient evidence to suggest that the null hypothesis is not true.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0621042 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0621042\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b7fdeef\" data-id=\"b7fdeef\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b9c8081 elementor-widget elementor-widget-heading\" data-id=\"b9c8081\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is p-value?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bbd4e95 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bbd4e95\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-812a125\" data-id=\"812a125\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e4bb68c elementor-widget elementor-widget-text-editor\" data-id=\"e4bb68c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In statistics, the p-value is a measure of the evidence against a null hypothesis. It represents the probability of obtaining a result as extreme or more extreme than the one observed, assuming that the null hypothesis is true.<\/p><p>More specifically, the p-value is the probability of observing a test statistic (such as a t-statistic or a z-statistic) as extreme or more extreme than the one observed, given the null hypothesis. If the p-value is small (usually less than 0.05 or 0.01), it indicates that the observed result is unlikely to have occurred by chance alone and the null hypothesis can be rejected.<\/p><p>For example, if we are testing whether a new drug is effective, the null hypothesis would be that the drug has no effect. If we conduct a clinical trial and find a small p-value (e.g., 0.03), it suggests that the observed improvement in the treatment group is unlikely to have occurred by chance alone, and we can reject the null hypothesis and conclude that the drug is effective.<\/p><p>It is important to note that the p-value is not the probability that the null hypothesis is true or false, but rather the probability of obtaining the observed result assuming that the null hypothesis is true. Therefore, a small p-value does not necessarily mean that the alternative hypothesis is true, but only that there is strong evidence against the null hypothesis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9797055 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9797055\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c27acda\" data-id=\"c27acda\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-01d830a elementor-widget elementor-widget-heading\" data-id=\"01d830a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is the statistical power of a test?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ccfa3d0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ccfa3d0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-58e56de\" data-id=\"58e56de\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-04378fd elementor-widget elementor-widget-text-editor\" data-id=\"04378fd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The statistical power of a test is the probability of correctly rejecting the null hypothesis when it is false. In other words, it is the probability of detecting a true effect or difference between two groups or variables.<\/p><p>A test with high statistical power is able to detect even small differences or effects, while a test with low statistical power is more likely to miss them.<\/p><p>Statistical power depends on several factors, including the sample size, the level of significance, and the effect size. Increasing the sample size or the level of significance can increase the statistical power of a test, while increasing the effect size (i.e., the magnitude of the difference between the groups or variables being compared) can also increase the statistical power.<\/p><p>It is important to consider the statistical power of a test when designing experiments or studies, as low statistical power can lead to false negative results (i.e., failing to detect a true effect) and reduce the overall reliability of the study. Therefore, researchers often conduct power analyses to estimate the required sample size and ensure that their study has adequate statistical power to detect the effects they are interested in.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fa4e248 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fa4e248\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-266b584\" data-id=\"266b584\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-efc721d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"efc721d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-ddea64e\" data-id=\"ddea64e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-be5a5f1 elementor-widget elementor-widget-heading\" data-id=\"be5a5f1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Discover IdSurvey<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-621eb80 elementor-widget elementor-widget-text-editor\" data-id=\"621eb80\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe most powerful survey software, loved by professionals.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6465e77 elementor-align-center yellow_btn elementor-widget elementor-widget-button\" data-id=\"6465e77\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/www.idsurvey.com\/en\/demo-request\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-icon\">\n\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-chevron-right\" viewBox=\"0 0 320 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M285.476 272.971L91.132 467.314c-9.373 9.373-24.569 9.373-33.941 0l-22.667-22.667c-9.357-9.357-9.375-24.522-.04-33.901L188.505 256 34.484 101.255c-9.335-9.379-9.317-24.544.04-33.901l22.667-22.667c9.373-9.373 24.569-9.373 33.941 0L285.475 239.03c9.373 9.372 9.373 24.568.001 33.941z\"><\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Request demo<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>AB testing calculator A\/B testing for statistical significance calculation. Is your A\/B testing statistically significant? Experiment with the settings and gain a deeper understanding of how decreasing the confidence level can enhance the effect size or how increasing the sample size can render a minor difference in the control and experimental groups statistically significant! Pre-test [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"wl_entities_gutenberg":"","footnotes":""},"wl_entity_type":[86],"class_list":["post-46052","page","type-page","status-publish","hentry","wl_entity_type-article"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.0 (Yoast SEO v26.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Survey A\/B testing calculator- IdSurvey<\/title>\n<meta name=\"description\" content=\"Optimize the response rate to your surveys with IdSurvey&#039;s A\/B testing calculator tool: try it for free and improve the performance of your campaigns.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.idsurvey.com\/en\/ab-testing-calculator\/\" \/>\n<meta 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