76 lines
3.6 KiB
HTML
76 lines
3.6 KiB
HTML
{% extends "layout.html" %} {% block content %}
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<main class="container">
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<section class="p-0 rounded bg-white mt-3 shadow-sm border">
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<h3 class="text-light py-2 pl-4 primary-colour-bg rounded-top">Test results</h3>
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<form class="mt-4 px-4" action="" method="post">
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{{ form.hidden_tag() }}
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{% if form.title.errors %}
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{{ form.title(class="py-2 form-control is-invalid") }}
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<div class="text-danger">
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{% for error in form.title.errors %}
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<small>{{ error }}</small>
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{% endfor %}
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</div>
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{% else %}
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{{ form.title(class="form-control") }}
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{% endif %}
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<table class="table table-bordered mt-3">
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<thead class="thead-light">
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<tr>
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<th class="help" scope="col"data-toggle="tooltip"
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title="This is the hypothesis that there are NO significant differences of characteristics between specifed populations.">
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Null Hypothesis</th>
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<th class="help" scope="col"data-toggle="tooltip"
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title="This is the type of statistical test used to test the null hypothesis.">
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Statistical Test</th>
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<th class="help" scope="col" data-toggle="tooltip" data-placement="left"
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title="Sometimes called 'Alpha', the significance value is the probability of rejecting the null hypothesis when it is true.
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It can be though of as a 'cut-off' point; when the p-value is lower you can reject the null hypothesis. We have set this to a default
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of 0.05 (indicating a 5% risk of no actual difference when concluding that a difference exists).">
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Significance value</th>
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<th class="help" scope="col" data-toggle="tooltip" data-placement="left"
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title="If we assume that the null hypothesis is true, the p-value gives the probability of obtaining the results as extreme as the
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observed results of the statistical test. This value is compared against the significance value, with a lower p-value meaning we
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can reject the null hypothesis.">
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P-Value</th>
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<th class="help" scope="col" data-toggle='tooltip'
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title='Whether we should reject or accept the null hypothesis.'>
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Conclusion</th>
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</tr>
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</thead>
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<tr class="primary-colour">
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{% if result["test"] == "Chi-Square goodness of fit" %}
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<td>There is no significant difference between the observed and the expected values.</td>
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{% elif result["test"] == "Chi-Square Test" %}
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<td>There is no association between <strong>{{ result["dv"] }}</strong> and <strong>{{ result["iv"] }}</strong>.</td>
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{% else %}
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<td>The distribution of <strong>{{ result["dv"] }}</strong> is the same across groups of <strong>{{ result["iv"] }}</strong></td>
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{% endif %}
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<td>{{ result["test"] }}</td>
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<td>{{ result["alpha"] }}</td>
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<td>{{ result["p"] }}</td>
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{% if result["p"] <= result["alpha"] %}
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<td>Reject the null hypothesis</td>
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{% else %}
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<td>Accept the null hypothesis</td>
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{% endif %}
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</tr>
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</table>
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<div class="d-flex justify-content-between mb-3">
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<a class="btn btn-secondary" href="{{ url_for('surveys.dashboard', survey_id=survey_id) }}"
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title="Cancel and return to the survey dashboard page">Cancel</a>
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{{ form.submit(class="btn btn-primary") }}
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</div>
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</form>
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</section>
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</main>
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<script type="text/javascript">
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$(function () {
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$("[data-toggle='tooltip']").tooltip();
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});
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</script>
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{% endblock content %}
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