webtests/hptx.html at main · CunMayday/webtests · GitHub

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<!-- v1: Created hypothesis setup guide for null and alternative hypotheses. -->
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
  <meta name="description" content="Interactive guide to help students prepare null and alternative hypotheses for hypothesis testing." />
  <title>Hypothesis Setup Companion</title>
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</head>
<body>
  <header>
    <h1>Hypothesis Testing Setup Companion</h1>
    <p role="doc-subtitle">Follow the guided steps to prepare valid null and alternative hypotheses.</p>
  </header>
  <main>
    <section class="intro" aria-label="Purpose of this guide">
      <h2>How to Use This Companion</h2>
      <p>
        Hypothesis testing begins with clearly defined hypotheses. This guided worksheet will walk you through clarifying your
        research question, identifying the correct population parameter, and crafting compliant null and alternative
        hypotheses. Each step summarizes key reminders from the hypothesis testing handbook so you can stay aligned with best
        practices.
      </p>
    </section>

    <form aria-describedby="summary" id="hypothesis-form">
      <section class="step" aria-labelledby="step1-title">
        <h2 id="step1-title">Step 1. Clarify Your Research Question</h2>
        <p>
          Keep your question focused on one measurable characteristic of a single population. Avoid wording that implies
          causes, relationships, or comparisons across groups.
        </p>
        <label for="research-question">What is your research question?</label>
        <textarea id="research-question" name="research-question" rows="3" aria-describedby="rq-help" required></textarea>
        <div id="rq-help" class="helper" role="note">
          <strong>Reminder:</strong> Look for language such as <em>causes</em>, <em>affects</em>, or <em>compared to</em>. If you
          find any, rephrase the question so it only describes one population characteristic.
        </div>
      </section>

      <section class="step" aria-labelledby="step2-title">
        <h2 id="step2-title">Step 2. Identify the Population Parameter</h2>
        <p>
          Determine whether you will describe a population mean (μ) or a population proportion (p). Choose one parameter only.
        </p>
        <fieldset>
          <legend>Select the parameter you will test</legend>
          <div>
            <input type="radio" id="param-mean" name="parameter" value="mean" required />
            <label for="param-mean">Population mean (μ): Use for numerical data like heights, prices, or hours.</label>
          </div>
          <div>
            <input type="radio" id="param-proportion" name="parameter" value="proportion" />
            <label for="param-proportion">Population proportion (p): Use for categorical data such as yes/no responses.</label>
          </div>
        </fieldset>
        <label for="null-value">What claimed value will you test against?</label>
        <input type="number" id="null-value" name="null-value" aria-describedby="null-value-help" step="any" required />
        <div id="null-value-help" class="tips">
          <h3>Choose a specific value</h3>
          <ul>
            <li>Draw from historical claims, previous studies, or neutral benchmarks.</li>
            <li>Avoid ranges or vague descriptions—hypotheses require an exact number.</li>
          </ul>
        </div>
      </section>

      <section class="step" aria-labelledby="step3-title">
        <h2 id="step3-title">Step 3. Confirm Your Data Is Obtainable</h2>
        <p>
          Ensure you can gather a sample ethically, with the resources you have available, and in a way that yields reliable
          measurements.
        </p>
        <label for="feasibility">Describe how you will collect the data.</label>
        <textarea id="feasibility" name="feasibility" rows="3" aria-describedby="feasibility-help" required></textarea>
        <div id="feasibility-help" class="helper">
          <strong>Checklist:</strong> Is the data accessible? Can you measure it objectively? Do you have permission to collect
          it? If not, adjust your question before moving forward.
        </div>
      </section>

      <section class="step" aria-labelledby="step4-title">
        <h2 id="step4-title">Step 4. Build the Null Hypothesis (H₀)</h2>
        <p>
          The null hypothesis always uses an equals sign and states that the population parameter equals the value you selected.
        </p>
        <div class="tips" role="note">
          <h3>Format reminder</h3>
          <p>H₀: parameter = value</p>
          <ul>
            <li>There is no &lt;, &gt;, or ≠ in the null hypothesis.</li>
            <li>Think of this as the “no change” or “status quo” position.</li>
          </ul>
        </div>
      </section>

      <section class="step" aria-labelledby="step5-title">
        <h2 id="step5-title">Step 5. Choose Your Alternative Hypothesis (H₁)</h2>
        <p>
          Decide whether you are testing for any difference (two-tailed) or a specific direction (one-tailed). When unsure,
          select the two-tailed option.
        </p>
        <fieldset>
          <legend>What kind of difference are you testing?</legend>
          <div>
            <input type="radio" id="alt-not-equal" name="alt" value="not-equal" required />
            <label for="alt-not-equal">Two-tailed: H₁ states the parameter is different (≠) from the null value.</label>
          </div>
          <div>
            <input type="radio" id="alt-greater" name="alt" value="greater" />
            <label for="alt-greater">Upper-tailed: H₁ states the parameter is greater than (&gt;) the null value.</label>
          </div>
          <div>
            <input type="radio" id="alt-less" name="alt" value="less" />
            <label for="alt-less">Lower-tailed: H₁ states the parameter is less than (&lt;) the null value.</label>
          </div>
        </fieldset>
        <div class="tips">
          <h3>Directional choice tips</h3>
          <ul>
            <li>Use ≠ when you want to detect any difference.</li>
            <li>Use &gt; only with a strong reason to look for an increase.</li>
            <li>Use &lt; only with a strong reason to look for a decrease.</li>
          </ul>
        </div>
      </section>

      <section class="step" aria-labelledby="step6-title">
        <h2 id="step6-title">Step 6. Select the Test Type</h2>
        <p>
          Match your hypotheses to the correct statistical procedure before collecting data.
        </p>
        <fieldset>
          <legend>Recommended test</legend>
          <p id="test-guidance">Select a parameter above to view the suggested test.</p>
        </fieldset>
      </section>

      <section class="step" aria-labelledby="step7-title">
        <h2 id="step7-title">Step 7. Pick a Significance Level (α)</h2>
        <p>
          Choose your tolerance for false alarms before you analyze data. Classroom projects typically use α = 0.05.
        </p>
        <label for="alpha">Significance level (α)</label>
        <select id="alpha" name="alpha" aria-describedby="alpha-help" required>
          <option value="0.05">0.05 (standard classroom choice)</option>
          <option value="0.01">0.01 (more cautious)</option>
          <option value="0.10">0.10 (more exploratory)</option>
        </select>
        <div id="alpha-help" class="helper">
          <strong>Interpretation:</strong> α is the chance of rejecting a true null hypothesis. Decide upfront and stick with it.
        </div>
      </section>

      <button type="button" id="generate">Generate Hypotheses Summary</button>
    </form>

    <section class="output-card" aria-live="polite" aria-atomic="true" id="summary" tabindex="-1">
      <h2>Your Draft Hypotheses</h2>
      <p id="null-output">H₀: —</p>
      <p id="alt-output">H₁: —</p>
      <p id="test-output">Suggested test: —</p>
      <p id="alpha-output">Chosen α: —</p>
      <p id="notes-output" class="sr-only">Complete each step to produce a full summary.</p>
    </section>
  </main>
  <footer style="background-color: var(--purdue-black); color: var(--white); text-align: center; padding: 1rem 0;">
    <p>&copy; <span id="year"></span> Hypothesis Testing Companion. Built with accessibility and Purdue University colors in mind.</p>
  </footer>

  <script>
    (function () {
      const form = document.getElementById('hypothesis-form');
      const generateBtn = document.getElementById('generate');
      const summaryCard = document.getElementById('summary');
      const nullOutput = document.getElementById('null-output');
      const altOutput = document.getElementById('alt-output');
      const testOutput = document.getElementById('test-output');
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      const notesOutput = document.getElementById('notes-output');
      const testGuidance = document.getElementById('test-guidance');
      const yearSpan = document.getElementById('year');

      yearSpan.textContent = new Date().getFullYear();

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            testGuidance.textContent = 'One-sample t-test: use when working with numerical data and a population mean (μ).';
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        const parameterSymbol = parameter === 'mean' ? 'μ' : 'p';
        let altSymbol = '≠';

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          altSymbol = '>';
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        nullOutput.textContent = `H₀: ${parameterSymbol} = ${nullValue}`;
        altOutput.textContent = `H₁: ${parameterSymbol} ${altSymbol} ${nullValue}`;

        if (parameter === 'mean') {
          testOutput.textContent = 'Suggested test: One-sample t-test for the population mean.';
        } else {
          testOutput.textContent = 'Suggested test: One-sample proportion test for the population proportion.';
        }

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</body>
</html>