cd /tmp/workspace && python3 << 'EOF' from pptx import Presentation from pptx.util import Emu from pptx.enum.shapes import MSO_SHAPE_TYPE import os pptx_path = "/tmp/workspace/attachments/c20ae8a2-a002-4c6b-9092-dbec28d9d9b9/seminar 5 (1).pptx" prs = Presentation(pptx_path) print(f"Total slides: {len(prs.slides)}") print() for i, slide in enumerate(prs.slides, 1): print(f"=== SLIDE {i} (layout: {slide.slide_layout.name}) ===") for shape in slide.shapes: pos = None if shape.left is not None: pos = (round(Emu(shape.left).inches, 2), round(Emu(shape.top).inches, 2)) size = (round(Emu(shape.width).inches, 2), round(Emu(shape.height).inches, 2)) print(f" Shape: {shape.shape_type} | Name: {shape.name} | Pos: {pos} | Size: {size if shape.left else 'N/A'}") if shape.has_text_frame and shape.text_frame.text.strip(): print(f" Text: {shape.text_frame.text[:200]}") if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: print(f" *** IMAGE FOUND ***") print() EOF

cd /tmp/workspace && python3 << 'EOF' from pptx import Presentation from pptx.enum.shapes import MSO_SHAPE_TYPE import os pptx_path = "/tmp/workspace/attachments/c20ae8a2-a002-4c6b-9092-dbec28d9d9b9/seminar 5 (1).pptx" prs = Presentation(pptx_path) os.makedirs("/tmp/workspace/pptx_images", exist_ok=True) for i, slide in enumerate(prs.slides, 1): img_count = 0 for shape in slide.shapes: if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: img_count += 1 img = shape.image ext = img.ext fname = f"/tmp/workspace/pptx_images/slide{i}_img{img_count}.{ext}" with open(fname, "wb") as f: f.write(img.blob) print(f"Saved: {fname} ({len(img.blob)} bytes)") print("\nDone!") EOF

You need to search for and download high-quality, clearly explaining educational/diagram images for a statistics seminar PPT on "Test of Significance" for dental/medical students. For each topic below, search the web and find a good image URL (preferably from Wikipedia, Wikimedia Commons, or educational sites), then download it to the specified path using curl or wget. The images should be clean diagrams or infographics - NOT photos. Topics and target file paths: 1. **Slide 3 - Introduction to Statistics in dentistry**: A clean infographic or diagram showing "role of statistics in research/dentistry" or "importance of statistics in healthcare" - Save to: /tmp/workspace/new_images/slide3.png 2. **Slide 5 - Pathway/Steps of Statistics (research process flowchart)**: A diagram showing the steps of statistical analysis / research methodology flowchart (data collection → analysis → conclusion) - Save to: /tmp/workspace/new_images/slide5.png 3. **Slide 6 - Classification of Statistics (Descriptive vs Inferential)**: A clear diagram/chart showing "Types of statistics" - descriptive (mean, median, mode) vs inferential statistics - Save to: /tmp/workspace/new_images/slide6.png 4. **Slide 7 - Classification continued (Types of data - nominal, ordinal, interval, ratio)**: A diagram showing levels/types of measurement data - Save to: /tmp/workspace/new_images/slide7.png 5. **Slide 9 - Hypothesis Testing (Null vs Alternative)**: A diagram showing null hypothesis H0 vs alternative hypothesis H1, hypothesis testing concept - Save to: /tmp/workspace/new_images/slide9.jpg 6. **Slide 10 - Level of Significance (alpha = 0.05, 0.01)**: A normal distribution bell curve with shaded rejection regions showing alpha = 0.05 critical region - Save to: /tmp/workspace/new_images/slide10a.jpg (left image) - Save to: /tmp/workspace/new_images/slide10b.jpg (right image - same or similar) 7. **Slide 11 - P-value**: A bell curve diagram clearly showing p-value concept with shaded area under the curve - Save to: /tmp/workspace/new_images/slide11a.jpg (left image) - Save to: /tmp/workspace/new_images/slide11b.jpg (right image) 8. **Slide 12 - Parametric vs Non-parametric comparison table/diagram**: A clear comparison chart showing parametric vs nonparametric tests side by side - Save to: /tmp/workspace/new_images/slide12.png 9. **Slide 13 - Parametric test assumptions (normal distribution)**: A bell/normal distribution curve diagram - Save to: /tmp/workspace/new_images/slide13.jpg 10. **Slide 14 - Z-test diagram and T-test types diagram**: Z-distribution and t-distribution comparison, or types of t-test (independent, paired) - Save to: /tmp/workspace/new_images/slide14a.jpg (z-test) - Save to: /tmp/workspace/new_images/slide14b.png (t-test types) 11. **Slide 16 - ANOVA/F-test**: A diagram showing ANOVA - comparing 3 or more groups with box plots or bar charts showing between-group vs within-group variance - Save to: /tmp/workspace/new_images/slide16.jpg 12. **Slide 17 - One-tailed vs Two-tailed test**: A clear side-by-side diagram comparing one-tailed (left/right) vs two-tailed tests on a normal distribution curve - Save to: /tmp/workspace/new_images/slide17.png 13. **Slide 18 - Chi-Square test**: A chi-square distribution diagram or 2x2 contingency table diagram - Save to: /tmp/workspace/new_images/slide18.jpg **Strategy:** 1. First create the output directory: `mkdir -p /tmp/workspace/new_images` 2. For each topic, use web_search to find good image URLs from reliable educational sources (Wikipedia, Wikimedia Commons, Khan Academy, etc.) 3. Download using: `curl -L -o /path/to/save "URL"` with a 30-second timeout 4. Verify the file was downloaded and has reasonable size (>10KB) 5. If one URL fails, try another For Wikimedia Commons images, use the direct image URL format. **Return a summary** of: - Which images were successfully downloaded (path + source URL) - Which images failed - Any notes about image quality

mkdir -p /tmp/workspace/new_images && echo "Directory created successfully"

role of statistics in dentistry research infographic diagram wikimedia commons

research methodology flowchart statistical analysis steps diagram wikimedia commons

descriptive vs inferential statistics diagram types of statistics wikimedia

levels of measurement nominal ordinal interval ratio diagram wikimedia commons

null hypothesis alternative hypothesis H0 H1 hypothesis testing diagram wikimedia

level of significance alpha 0.05 critical region rejection region bell curve normal distribution diagram wikimedia

p-value bell curve diagram shaded area statistics wikimedia commons

parametric vs nonparametric tests comparison table diagram statistics wikimedia

normal distribution bell curve diagram wikimedia commons statistics

z-distribution t-distribution comparison diagram statistics wikimedia commons

ANOVA one way analysis variance between groups within groups diagram box plot wikimedia

one-tailed vs two-tailed test normal distribution diagram side by side wikimedia commons

chi-square distribution diagram statistics wikimedia commons

types of t-test independent paired two sample diagram wikimedia commons

statistics in healthcare medical research importance diagram infographic educational site

# Slide 5: Research methodology flowchart from Wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide5.png "https://upload.wikimedia.org/wikipedia/commons/0/0d/Methodology_graph.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide5.png 2>/dev/null

# Slide 7: Levels of measurement diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide7.png "https://www.statology.org/wp-content/uploads/2020/01/levels_measurement.jpg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide7.png 2>/dev/null

# Slide 6: Descriptive vs Inferential statistics curl -L --max-time 30 -o /tmp/workspace/new_images/slide6.png "https://cdn1.qualitygurus.com/wp-content/uploads/2023/03/Descriptive-vs-Inferential-Statistics-1200x675.jpg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide6.png 2>/dev/null

# Slide 9: Hypothesis testing - H0 vs H1 curl -L --max-time 30 -o /tmp/workspace/new_images/slide9.jpg "https://images.squarespace-cdn.com/content/v1/5acbdd3a25bf024c12f4c8b4/1610052858922-VSB5QRBL1A1I4JQ5WJO6/H0+and+H1+Curves.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide9.jpg 2>/dev/null

# Slide 10a: Level of significance - critical region bell curve curl -L --max-time 30 -o /tmp/workspace/new_images/slide10a.jpg "https://i0.wp.com/statisticsbyjim.com/wp-content/uploads/2017/04/critical_region_05.png?resize=616%2C411" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide10a.jpg 2>/dev/null

# Slide 10b: Rejection region diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide10b.jpg "https://www.statisticshowto.com/wp-content/uploads/2022/07/Rejection_region.svg_.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide10b.jpg 2>/dev/null

# slide10a.jpg is only 5.8KB - try a different URL for critical region # Try the Wikimedia commons version of statistical significance curl -L --max-time 30 -o /tmp/workspace/new_images/slide10a.jpg "https://articles.outlier.org/_next/image?url=https%3A%2F%2Fimages.ctfassets.net%2Fkj4bmrik9d6o%2F3KBbY7DmgJBQMWqfR754vZ%2Fec92e7e60af90d66206f2b17f34dcacf%2FCritical_Values_04.png&w=3840&q=75" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide10a.jpg 2>/dev/null

# Slide 11a: P-value diagram - Wikimedia SVG converted curl -L --max-time 30 -o /tmp/workspace/new_images/slide11a.jpg "https://upload.wikimedia.org/wikipedia/en/0/00/P-value_Graph.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide11a.jpg 2>/dev/null

# Slide 11b: P-value second image - normal distribution with shading curl -L --max-time 30 -o /tmp/workspace/new_images/slide11b.jpg "https://upload.wikimedia.org/wikipedia/commons/9/94/A-normal-distribution-with-areas-under-curve-1.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide11b.jpg 2>/dev/null

# Slide 12: Parametric vs Non-parametric curl -L --max-time 30 -o /tmp/workspace/new_images/slide12.png "https://www.statisticalaid.com/wp-content/uploads/2025/05/Parametric-vs-Non-parametric-test-1.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide12.png 2>/dev/null

# Slide 13: Normal distribution bell curve - Wikimedia Standard deviation diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide13.jpg "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/1280px-Standard_deviation_diagram.svg.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide13.jpg 2>/dev/null

# Slide 14a: Z-distribution vs T-distribution comparison - Wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide14a.jpg "https://upload.wikimedia.org/wikipedia/commons/2/2a/Comparing_the_Standard_Normal_Distribution_and_Student%27s_T_Distribution.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide14a.jpg 2>/dev/null

# Slide 14b: Types of t-test diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide14b.png "https://www.researchgate.net/publication/334675856/figure/fig1/AS:800173106151427@1567787543807/Types-of-2-sample-t-test.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide14b.png 2>/dev/null

# Slide 16: ANOVA box plot diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide16.jpg "https://miro.medium.com/v2/resize:fit:1200/1*HHcq-3o1iRMNMsyBoWLOkw.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide16.jpg 2>/dev/null

# Slide 17: One-tailed vs two-tailed test diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide17.png "https://keydifferences.com/wp-content/uploads/2017/01/one-tailed-vs-two-tailed-test.jpg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide17.png 2>/dev/null

# Slide 18: Chi-square distribution diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide18.jpg "https://upload.wikimedia.org/wikipedia/commons/thumb/3/35/Chi-square_pdf.svg/1280px-Chi-square_pdf.svg.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide18.jpg 2>/dev/null

# Retry slide14b - types of t-test from Wikipedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide14b.png "https://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/2_Sample_Test.png/250px-2_Sample_Test.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide14b.png 2>/dev/null

# Now handle slide3 - use wikimedia statistics infographic # Try data visualization process diagram from wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide3.png "https://upload.wikimedia.org/wikipedia/commons/b/ba/Data_visualization_process_v1.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide3.png 2>/dev/null

# Check what we have so far ls -lh /tmp/workspace/new_images/

# Verify files using file command to ensure they're real images file /tmp/workspace/new_images/*.jpg /tmp/workspace/new_images/*.png 2>/dev/null

# slide11a is a bit small (415x274) - try Wikimedia P-value SVG PNG version curl -L --max-time 30 -o /tmp/workspace/new_images/slide11a.jpg "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/960px-Standard_deviation_diagram.svg.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide11a.jpg file /tmp/workspace/new_images/slide11a.jpg

# slide14b is small (250x152) - try full size t-distribution types curl -L --max-time 30 -o /tmp/workspace/new_images/slide14b.png "https://upload.wikimedia.org/wikipedia/commons/1/1c/2_Sample_Test.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide14b.png file /tmp/workspace/new_images/slide14b.png

# Let me try a better ANOVA diagram from PMC # Also try a dedicated statistics in research/dentistry image for slide3 # Try statistics in medical research diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide3.png "https://upload.wikimedia.org/wikipedia/commons/c/c7/The_Scientific_Method.jpg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide3.png file /tmp/workspace/new_images/slide3.png

# Try ANOVA concept image showing between/within variance curl -L --max-time 30 -o /tmp/workspace/new_images/slide16.jpg "https://datascienceplus.com/wp-content/uploads/2017/09/Unknown3.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide16.jpg file /tmp/workspace/new_images/slide16.jpg

# Let me try a better slide3 - a more descriptive statistics in research diagram # Try from geeksforgeeks or similar - the statistics overview curl -L --max-time 30 -A "Mozilla/5.0" -o /tmp/workspace/new_images/slide3.png "https://media.geeksforgeeks.org/wp-content/uploads/20250222144920592095/stat.webp" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide3.png file /tmp/workspace/new_images/slide3.png

# Convert webp to png using ImageMagick or check if we can use it directly # The webp is valid but let's try to get a proper PNG # Let's try a different approach - get an image about statistics in research from wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide3.png "https://upload.wikimedia.org/wikipedia/commons/thumb/7/74/Normal_Distribution_PDF.svg/960px-Normal_Distribution_PDF.svg.png" 2>&1 echo "Exit code: $?" # Actually that's a normal distribution - not ideal for slide3 # Let's try scientific method flowchart from Wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide3_alt.png "https://upload.wikimedia.org/wikipedia/commons/c/c7/The_Scientific_Method.jpg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide3*.png 2>/dev/null

file /tmp/workspace/new_images/slide3.png /tmp/workspace/new_images/slide3_alt.png

statistics medical dental research role importance diagram wikimedia commons educational PNG image

# Better slide3 - Research process from Wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide3.png "https://upload.wikimedia.org/wikipedia/commons/thumb/8/8f/Research_process.png/1280px-Research_process.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide3.png file /tmp/workspace/new_images/slide3.png

# Also try a better slide5 - the Methodology graph might not show data collection -> analysis -> conclusion well # Try the research design and evidence diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide5_alt.png "https://upload.wikimedia.org/wikipedia/commons/thumb/3/36/Research_design_and_evidence.svg/960px-Research_design_and_evidence.svg.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide5_alt.png file /tmp/workspace/new_images/slide5_alt.png

# slide5_alt looks more relevant - let's check existing slide5 # The slide5 (Methodology_graph.png) is a methodology chart - let's keep both and decide # Actually the new slide5_alt (Research design and evidence) is better for the statistics pathway # Let's also check if slide 11a needs improvement for p-value concept # The current slide11a is standard deviation diagram which is somewhat related but not p-value specifically # Let's get the actual p-value wikimedia image curl -L --max-time 30 -o /tmp/workspace/new_images/slide11a.jpg "https://upload.wikimedia.org/wikipedia/commons/a/a1/Chi-square_distribution_p-value_region_df7.svg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide11a.jpg file /tmp/workspace/new_images/slide11a.jpg

# The SVG won't render as jpg in a PPT - we need the rasterized version # Get the PNG version of the p-value diagram curl -L --max-time 30 -o /tmp/workspace/new_images/slide11a.jpg "https://upload.wikimedia.org/wikipedia/commons/thumb/a/a1/Chi-square_distribution_p-value_region_df7.svg/1280px-Chi-square_distribution_p-value_region_df7.svg.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide11a.jpg file /tmp/workspace/new_images/slide11a.jpg

# Now let's move slide5_alt to slide5 (better image for statistics steps) mv /tmp/workspace/new_images/slide5_alt.png /tmp/workspace/new_images/slide5_v2.png # Let's try to get a more specific p-value normal bell curve for slide11a # Get the actual P-value in statistical significance testing from Wikimedia curl -L --max-time 30 -o /tmp/workspace/new_images/slide11a_v2.jpg "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/P-value_in_statistical_significance_testing.svg/1280px-P-value_in_statistical_significance_testing.svg.png" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide11a_v2.jpg 2>/dev/null file /tmp/workspace/new_images/slide11a_v2.jpg 2>/dev/null

# Great! slide11a_v2 is a proper p-value diagram from Wikimedia - use this! cp /tmp/workspace/new_images/slide11a_v2.jpg /tmp/workspace/new_images/slide11a.jpg # Final check - compile all images echo "=== FINAL IMAGE STATUS ===" ls -lh /tmp/workspace/new_images/slide*.jpg /tmp/workspace/new_images/slide*.png 2>/dev/null | grep -v "_v2\|_alt\|_alt2"

# Run a full file type check on all primary images file /tmp/workspace/new_images/slide3.png \ /tmp/workspace/new_images/slide5.png \ /tmp/workspace/new_images/slide6.png \ /tmp/workspace/new_images/slide7.png \ /tmp/workspace/new_images/slide9.jpg \ /tmp/workspace/new_images/slide10a.jpg \ /tmp/workspace/new_images/slide10b.jpg \ /tmp/workspace/new_images/slide11a.jpg \ /tmp/workspace/new_images/slide11b.jpg \ /tmp/workspace/new_images/slide12.png \ /tmp/workspace/new_images/slide13.jpg \ /tmp/workspace/new_images/slide14a.jpg \ /tmp/workspace/new_images/slide14b.png \ /tmp/workspace/new_images/slide16.jpg \ /tmp/workspace/new_images/slide17.png \ /tmp/workspace/new_images/slide18.jpg

# slide9.jpg is WebP - let's get a better hypothesis testing image # Try the researchgate or a different source curl -L --max-time 30 -o /tmp/workspace/new_images/slide9_v2.jpg "https://miro.medium.com/v2/resize:fit:1200/0*BK_DNNtWwLK8HtkW.jpg" 2>&1 echo "Exit code: $?" ls -lh /tmp/workspace/new_images/slide9_v2.jpg file /tmp/workspace/new_images/slide9_v2.jpg

# Also try to get a better slide5 - the current one is a methodology graph which is fine # but slide5_v2 (Research design and evidence) might be better file /tmp/workspace/new_images/slide5_v2.png ls -lh /tmp/workspace/new_images/slide5_v2.png

# slide9_v2 is a proper JPEG at 1200x899 - use this! cp /tmp/workspace/new_images/slide9_v2.jpg /tmp/workspace/new_images/slide9.jpg # Use slide5_v2 as slide5 (research design and evidence - better for statistics pathway) cp /tmp/workspace/new_images/slide5_v2.png /tmp/workspace/new_images/slide5.png echo "=== FINAL COMPLETE STATUS ===" for f in slide3.png slide5.png slide6.png slide7.png slide9.jpg slide10a.jpg slide10b.jpg slide11a.jpg slide11b.jpg slide12.png slide13.jpg slide14a.jpg slide14b.png slide16.jpg slide17.png slide18.jpg; do size=$(ls -lh /tmp/workspace/new_images/$f 2>/dev/null | awk '{print $5}') ftype=$(file /tmp/workspace/new_images/$f 2>/dev/null | cut -d: -f2 | xargs | cut -c1-60) if [ -f "/tmp/workspace/new_images/$f" ]; then echo "✓ $f | $size | $ftype" else echo "✗ $f MISSING" fi done

# Final summary with all sizes confirmed echo "=== TOTAL FILES ===" ls /tmp/workspace/new_images/slide3.png /tmp/workspace/new_images/slide5.png /tmp/workspace/new_images/slide6.png /tmp/workspace/new_images/slide7.png /tmp/workspace/new_images/slide9.jpg /tmp/workspace/new_images/slide10a.jpg /tmp/workspace/new_images/slide10b.jpg /tmp/workspace/new_images/slide11a.jpg /tmp/workspace/new_images/slide11b.jpg /tmp/workspace/new_images/slide12.png /tmp/workspace/new_images/slide13.jpg /tmp/workspace/new_images/slide14a.jpg /tmp/workspace/new_images/slide14b.png /tmp/workspace/new_images/slide16.jpg /tmp/workspace/new_images/slide17.png /tmp/workspace/new_images/slide18.jpg | wc -l echo "16 expected" echo "" echo "=== ALL SIZE CHECKS (must be > 10K) ===" for f in /tmp/workspace/new_images/slide3.png /tmp/workspace/new_images/slide5.png /tmp/workspace/new_images/slide6.png /tmp/workspace/new_images/slide7.png /tmp/workspace/new_images/slide9.jpg /tmp/workspace/new_images/slide10a.jpg /tmp/workspace/new_images/slide10b.jpg /tmp/workspace/new_images/slide11a.jpg /tmp/workspace/new_images/slide11b.jpg /tmp/workspace/new_images/slide12.png /tmp/workspace/new_images/slide13.jpg /tmp/workspace/new_images/slide14a.jpg /tmp/workspace/new_images/slide14b.png /tmp/workspace/new_images/slide16.jpg /tmp/workspace/new_images/slide17.png /tmp/workspace/new_images/slide18.jpg; do size_bytes=$(wc -c < "$f") name=$(basename $f) if [ $size_bytes -gt 10240 ]; then echo "✓ PASS $name ($size_bytes bytes)" else echo "✗ FAIL $name ($size_bytes bytes) - TOO SMALL" fi done

ls /tmp/workspace/new_images/

cd /tmp/workspace && python3 << 'EOF' """ Replace images in the PPTX with better explaining images. Strategy: Replace each picture shape's image data with the new image file. We keep the same position and size as the original image. """ import copy, os from pptx import Presentation from pptx.enum.shapes import MSO_SHAPE_TYPE from pptx.util import Emu, Inches, Pt from pptx.oxml.ns import qn from lxml import etree import shutil pptx_path = "/tmp/workspace/attachments/c20ae8a2-a002-4c6b-9092-dbec28d9d9b9/seminar 5 (1).pptx" out_path = "/tmp/workspace/seminar5_updated.pptx" # Copy original first shutil.copy(pptx_path, out_path) prs = Presentation(out_path) IMG_DIR = "/tmp/workspace/new_images" # Map: (slide_number, image_index_on_slide) -> new image path # slide_number is 1-indexed, image_index is 1-indexed per slide replacements = { (3, 1): f"{IMG_DIR}/slide3.png", (5, 1): f"{IMG_DIR}/slide5.png", (6, 1): f"{IMG_DIR}/slide6.png", (7, 1): f"{IMG_DIR}/slide7.png", (9, 1): f"{IMG_DIR}/slide9.jpg", (10, 1): f"{IMG_DIR}/slide10a.jpg", (10, 2): f"{IMG_DIR}/slide10b.jpg", (11, 1): f"{IMG_DIR}/slide11a.jpg", (11, 2): f"{IMG_DIR}/slide11b.jpg", (12, 1): f"{IMG_DIR}/slide12.png", (13, 1): f"{IMG_DIR}/slide13.jpg", (14, 1): f"{IMG_DIR}/slide14a.jpg", (14, 2): f"{IMG_DIR}/slide14b.png", (16, 1): f"{IMG_DIR}/slide16.jpg", (17, 1): f"{IMG_DIR}/slide17.png", (18, 1): f"{IMG_DIR}/slide18.jpg", } def replace_picture(slide, shape, new_img_path): """Replace the image data in a picture shape with new image data.""" from pptx.parts.image import Image from pptx.opc.part import Part import mimetypes # Read new image bytes with open(new_img_path, "rb") as f: new_bytes = f.read() # Get the image part relationship blip_elem = shape._element.find('.//' + qn('a:blip')) if blip_elem is None: print(f" WARNING: No blip found in shape {shape.name}") return False rId = blip_elem.get(qn('r:embed')) if not rId: print(f" WARNING: No rId in blip for shape {shape.name}") return False # Get the image part from the relationship img_part = slide.part.related_parts[rId] # Determine content type from extension ext = os.path.splitext(new_img_path)[1].lower() ct_map = { '.png': 'image/png', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', '.gif': 'image/gif', '.bmp': 'image/bmp', '.tiff': 'image/tiff', '.wmf': 'image/x-wmf', } new_ct = ct_map.get(ext, 'image/png') # Replace the blob (image data) img_part._blob = new_bytes img_part.content_type = new_ct return True # Process each slide for slide_idx, slide in enumerate(prs.slides, 1): img_count = 0 for shape in slide.shapes: if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: img_count += 1 key = (slide_idx, img_count) if key in replacements: new_path = replacements[key] if os.path.exists(new_path): success = replace_picture(slide, shape, new_path) if success: print(f"✅ Slide {slide_idx}, img {img_count}: replaced with {os.path.basename(new_path)}") else: print(f"❌ Slide {slide_idx}, img {img_count}: FAILED to replace") else: print(f"⚠️ Slide {slide_idx}, img {img_count}: file not found: {new_path}") prs.save(out_path) print(f"\n✅ Saved updated presentation to: {out_path}") EOF

cd /tmp/workspace && python3 << 'EOF' """ Replace images using the unpack/edit XML approach - more reliable. We'll use python-pptx to replace image blobs directly via the image part's blob attribute. """ import os, shutil from pptx import Presentation from pptx.enum.shapes import MSO_SHAPE_TYPE from pptx.oxml.ns import qn pptx_path = "/tmp/workspace/attachments/c20ae8a2-a002-4c6b-9092-dbec28d9d9b9/seminar 5 (1).pptx" out_path = "/tmp/workspace/seminar5_updated.pptx" shutil.copy(pptx_path, out_path) prs = Presentation(out_path) IMG_DIR = "/tmp/workspace/new_images" replacements = { (3, 1): f"{IMG_DIR}/slide3.png", (5, 1): f"{IMG_DIR}/slide5.png", (6, 1): f"{IMG_DIR}/slide6.png", (7, 1): f"{IMG_DIR}/slide7.png", (9, 1): f"{IMG_DIR}/slide9.jpg", (10, 1): f"{IMG_DIR}/slide10a.jpg", (10, 2): f"{IMG_DIR}/slide10b.jpg", (11, 1): f"{IMG_DIR}/slide11a.jpg", (11, 2): f"{IMG_DIR}/slide11b.jpg", (12, 1): f"{IMG_DIR}/slide12.png", (13, 1): f"{IMG_DIR}/slide13.jpg", (14, 1): f"{IMG_DIR}/slide14a.jpg", (14, 2): f"{IMG_DIR}/slide14b.png", (16, 1): f"{IMG_DIR}/slide16.jpg", (17, 1): f"{IMG_DIR}/slide17.png", (18, 1): f"{IMG_DIR}/slide18.jpg", } def replace_picture_blob(slide, shape, new_img_path): """Replace image blob by accessing the image part directly.""" blip_elem = shape._element.find('.//' + qn('a:blip')) if blip_elem is None: return False, "No blip element" rId = blip_elem.get(qn('r:embed')) if not rId: return False, "No rId" # Get image part img_part = slide.part.related_parts[rId] with open(new_img_path, "rb") as f: new_bytes = f.read() # Update content type ext = os.path.splitext(new_img_path)[1].lower() ct_map = {'.png': 'image/png', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg'} new_ct = ct_map.get(ext, 'image/png') # Replace the blob img_part._blob = new_bytes # Try to update content type on the part object try: img_part._content_type = new_ct except: pass try: img_part.content_type = new_ct except: pass return True, "OK" for slide_idx, slide in enumerate(prs.slides, 1): img_count = 0 for shape in slide.shapes: if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: img_count += 1 key = (slide_idx, img_count) if key in replacements: new_path = replacements[key] if os.path.exists(new_path): ok, msg = replace_picture_blob(slide, shape, new_path) status = "✅" if ok else "❌" print(f"{status} Slide {slide_idx}, img {img_count}: {os.path.basename(new_path)} - {msg}") else: print(f"⚠️ File missing: {new_path}") prs.save(out_path) print(f"\n✅ Saved to: {out_path}") print(f"File size: {os.path.getsize(out_path)} bytes") EOF

cd /tmp/workspace && python3 << 'EOF' # Explore python-pptx API for image replacement from pptx import Presentation from pptx.enum.shapes import MSO_SHAPE_TYPE from pptx.oxml.ns import qn import shutil pptx_path = "/tmp/workspace/attachments/c20ae8a2-a002-4c6b-9092-dbec28d9d9b9/seminar 5 (1).pptx" prs = Presentation(pptx_path) # Find first image slide (slide 3) slide = prs.slides[2] # slide 3 for shape in slide.shapes: if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: print("Shape name:", shape.name) print("Shape type:", type(shape)) print("Shape part:", type(shape.part)) blip = shape._element.find('.//' + qn('a:blip')) print("Blip:", blip) rid = blip.get(qn('r:embed')) print("rId:", rid) slide_part = shape.part print("slide_part dir:", [x for x in dir(slide_part) if 'rel' in x.lower()]) # Try related_part (singular) try: img_part = slide_part.related_part(rid) print("Image part type:", type(img_part)) print("Image part blob size:", len(img_part._blob)) print("Image part content_type:", img_part.content_type) print("Image part dir:", [x for x in dir(img_part) if not x.startswith('__')]) except Exception as e: print("related_part error:", e) break EOF

cd /tmp/workspace && python3 << 'EOF' """ Replace images using the correct python-pptx API: related_part() and _blob / _content_type """ import os, shutil from pptx import Presentation from pptx.enum.shapes import MSO_SHAPE_TYPE from pptx.oxml.ns import qn pptx_path = "/tmp/workspace/attachments/c20ae8a2-a002-4c6b-9092-dbec28d9d9b9/seminar 5 (1).pptx" out_path = "/tmp/workspace/seminar5_updated.pptx" shutil.copy(pptx_path, out_path) prs = Presentation(out_path) IMG_DIR = "/tmp/workspace/new_images" replacements = { (3, 1): f"{IMG_DIR}/slide3.png", (5, 1): f"{IMG_DIR}/slide5.png", (6, 1): f"{IMG_DIR}/slide6.png", (7, 1): f"{IMG_DIR}/slide7.png", (9, 1): f"{IMG_DIR}/slide9.jpg", (10, 1): f"{IMG_DIR}/slide10a.jpg", (10, 2): f"{IMG_DIR}/slide10b.jpg", (11, 1): f"{IMG_DIR}/slide11a.jpg", (11, 2): f"{IMG_DIR}/slide11b.jpg", (12, 1): f"{IMG_DIR}/slide12.png", (13, 1): f"{IMG_DIR}/slide13.jpg", (14, 1): f"{IMG_DIR}/slide14a.jpg", (14, 2): f"{IMG_DIR}/slide14b.png", (16, 1): f"{IMG_DIR}/slide16.jpg", (17, 1): f"{IMG_DIR}/slide17.png", (18, 1): f"{IMG_DIR}/slide18.jpg", } def replace_picture_blob(shape, new_img_path): """Replace image blob using correct python-pptx API.""" blip_elem = shape._element.find('.//' + qn('a:blip')) if blip_elem is None: return False, "No blip element" rId = blip_elem.get(qn('r:embed')) if not rId: return False, "No rId" # Get image part using related_part (singular) img_part = shape.part.related_part(rId) with open(new_img_path, "rb") as f: new_bytes = f.read() # Update content type based on extension ext = os.path.splitext(new_img_path)[1].lower() ct_map = {'.png': 'image/png', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg'} new_ct = ct_map.get(ext, 'image/png') # Replace the blob and content type img_part._blob = new_bytes img_part._content_type = new_ct return True, f"OK ({len(new_bytes)} bytes, {new_ct})" for slide_idx, slide in enumerate(prs.slides, 1): img_count = 0 for shape in slide.shapes: if shape.shape_type == MSO_SHAPE_TYPE.PICTURE: img_count += 1 key = (slide_idx, img_count) if key in replacements: new_path = replacements[key] if os.path.exists(new_path): ok, msg = replace_picture_blob(shape, new_path) status = "✅" if ok else "❌" print(f"{status} Slide {slide_idx}, img {img_count}: {os.path.basename(new_path)} - {msg}") else: print(f"⚠️ File missing: {new_path}") prs.save(out_path) print(f"\n✅ Saved to: {out_path}") print(f"File size: {os.path.getsize(out_path):,} bytes") EOF

python3 -c " from pptx import Presentation from pptx.enum.shapes import MSO_SHAPE_TYPE prs = Presentation('/tmp/workspace/seminar5_updated.pptx') print(f'Slides: {len(prs.slides)}') for i, slide in enumerate(prs.slides, 1): imgs = [s for s in slide.shapes if s.shape_type == MSO_SHAPE_TYPE.PICTURE] if imgs: for j, s in enumerate(imgs, 1): img_part = s.part.related_part(s._element.find('.//{http://schemas.openxmlformats.org/drawingml/2006/main}blip').get('{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed')) print(f' Slide {i}, img {j}: {img_part.content_type}, {len(img_part._blob):,} bytes') print('File is valid!') "

mkdir -p /tmp/workspace/summary-doc && python3 << 'EOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.oxml.ns import qn from docx.oxml import OxmlElement doc = Document() # ── Page margins ────────────────────────────────────────────────────────────── from docx.oxml import OxmlElement section = doc.sections[0] section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.15) section.right_margin = Inches(1.15) # ── Default styles ──────────────────────────────────────────────────────────── normal = doc.styles["Normal"] normal.font.name = "Calibri" normal.font.size = Pt(11) def set_heading_style(para, level, text, color_hex="1F4E79"): """Add a heading with custom color.""" para.clear() run = para.add_run(text) run.bold = True r, g, b = int(color_hex[0:2],16), int(color_hex[2:4],16), int(color_hex[4:6],16) run.font.color.rgb = RGBColor(r, g, b) sizes = {1: 20, 2: 15, 3: 13} run.font.size = Pt(sizes.get(level, 12)) para.paragraph_format.space_before = Pt(14 if level==1 else 10) para.paragraph_format.space_after = Pt(4) def add_h1(doc, text): p = doc.add_paragraph() set_heading_style(p, 1, text) return p def add_h2(doc, text): p = doc.add_paragraph() set_heading_style(p, 2, text, "2E74B5") return p def add_h3(doc, text): p = doc.add_paragraph() set_heading_style(p, 3, text, "404040") return p def add_body(doc, text): p = doc.add_paragraph(text) p.paragraph_format.space_after = Pt(4) return p def add_bullet(doc, text, level=0): p = doc.add_paragraph(style="List Bullet") p.add_run(text) p.paragraph_format.left_indent = Inches(0.3 * (level + 1)) p.paragraph_format.space_after = Pt(2) return p def add_table_2col(doc, rows_data, headers=None): """Create a simple bordered 2-column table.""" num_cols = len(rows_data[0]) if rows_data else 2 table = doc.add_table(rows=0, cols=num_cols) table.style = "Table Grid" # Header row if headers: hrow = table.add_row() for i, h in enumerate(headers): cell = hrow.cells[i] cell.text = h run = cell.paragraphs[0].runs[0] run.bold = True run.font.color.rgb = RGBColor(0xFF, 0xFF, 0xFF) # Set cell background tc = cell._tc tcPr = tc.get_or_add_tcPr() shd = OxmlElement("w:shd") shd.set(qn("w:val"), "clear") shd.set(qn("w:color"), "auto") shd.set(qn("w:fill"), "2E74B5") tcPr.append(shd) for row_data in rows_data: row = table.add_row() for i, val in enumerate(row_data): row.cells[i].text = str(val) doc.add_paragraph() # spacer return table # ═══════════════════════════════════════════════════════════════════════════════ # TITLE PAGE # ═══════════════════════════════════════════════════════════════════════════════ title_para = doc.add_paragraph() title_para.alignment = WD_ALIGN_PARAGRAPH.CENTER title_run = title_para.add_run("TEST OF SIGNIFICANCE") title_run.bold = True title_run.font.size = Pt(26) title_run.font.color.rgb = RGBColor(0x1F, 0x4E, 0x79) doc.add_paragraph() sub_para = doc.add_paragraph() sub_para.alignment = WD_ALIGN_PARAGRAPH.CENTER sub_run = sub_para.add_run("A Comprehensive Summary of Statistical Tests\nfor Medical and Dental Research") sub_run.font.size = Pt(13) sub_run.font.color.rgb = RGBColor(0x40, 0x40, 0x40) sub_run.italic = True doc.add_paragraph() auth_para = doc.add_paragraph() auth_para.alignment = WD_ALIGN_PARAGRAPH.CENTER auth_para.add_run("E. Prasanna Kumar | I MDS") doc.add_page_break() # ═══════════════════════════════════════════════════════════════════════════════ # 1. INTRODUCTION # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "1. Introduction") add_body(doc, "Statistics is the science of collecting, organising, analysing, interpreting, and presenting data. In medical and dental research, statistical tests help determine whether observed differences between groups are real (i.e., unlikely to be due to chance) or merely the result of random variation.") add_body(doc, "A test of significance is a formal procedure for deciding whether the data provide sufficient evidence against a null hypothesis. The outcome guides clinical decisions, research conclusions, and evidence-based practice.") # ═══════════════════════════════════════════════════════════════════════════════ # 2. HYPOTHESIS # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "2. Hypothesis") add_body(doc, "A hypothesis is a tentative statement predicting a relationship between variables.") add_h2(doc, "2.1 Null Hypothesis (H₀)") add_body(doc, "States that no difference or association exists between the groups being compared.") add_bullet(doc, "Example: There is no difference in plaque scores between two toothpaste formulations.") add_h2(doc, "2.2 Alternative Hypothesis (H₁)") add_body(doc, "States that a real difference or association does exist.") add_bullet(doc, "Example: The new toothpaste significantly reduces plaque scores compared to the standard.") add_h2(doc, "2.3 Types of Errors") add_table_2col(doc, [ ["Type I Error (α)", "Rejecting H₀ when it is actually true (false positive). Probability = level of significance (α)."], ["Type II Error (β)", "Failing to reject H₀ when it is actually false (false negative). Related to the power of the test (1 - β)."], ], headers=["Error Type", "Description"]) # ═══════════════════════════════════════════════════════════════════════════════ # 3. LEVEL OF SIGNIFICANCE & P-VALUE # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "3. Level of Significance and P-Value") add_h2(doc, "3.1 Level of Significance (α)") add_body(doc, "The threshold probability at which H₀ is rejected. Common values:") add_bullet(doc, "α = 0.05 (5%) — widely used in biological/dental research") add_bullet(doc, "α = 0.01 (1%) — used when stricter evidence is required") add_h2(doc, "3.2 P-Value") add_body(doc, "The p-value is the probability of obtaining the observed result (or more extreme) assuming H₀ is true.") add_table_2col(doc, [ ["p < 0.05", "Statistically significant — reject H₀"], ["p < 0.01", "Highly significant"], ["p ≥ 0.05", "Not significant — fail to reject H₀"], ], headers=["P-Value Range", "Interpretation"]) add_h2(doc, "3.3 One-Tailed vs Two-Tailed Tests") add_body(doc, "A one-tailed test evaluates an effect in one direction only (e.g., greater than). A two-tailed test evaluates effects in both directions (greater than or less than). Two-tailed tests are more conservative and are preferred in most clinical research.") # ═══════════════════════════════════════════════════════════════════════════════ # 4. PARAMETRIC TESTS # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "4. Parametric Tests") add_body(doc, "Parametric tests assume the data follow a known distribution (usually normal/Gaussian). They are more powerful when assumptions are met.") add_h3(doc, "Key Assumptions") add_bullet(doc, "Data measured on a continuous (interval or ratio) scale") add_bullet(doc, "Sample drawn from a normally distributed population") add_bullet(doc, "Homogeneity of variance between groups") add_bullet(doc, "Random sampling") doc.add_paragraph() add_h2(doc, "4.1 Z-Test") add_table_2col(doc, [ ["Use when", "Sample size > 30 and population variance is known"], ["Formula", "Z = (X̄ - μ) / (σ / √n)"], ["Applications", "Comparing treatment outcomes; comparing two large independent samples; clinical trials"], ["Advantages", "Simple to compute; well-established critical values"], ["Limitations", "Requires known population SD; less useful for small samples"], ], headers=["Aspect", "Detail"]) add_h2(doc, "4.2 Student's t-Test") add_body(doc, "Used when sample size < 30 or the population standard deviation is unknown.") add_table_2col(doc, [ ["Independent samples t-test", "Compares means of two unrelated groups (e.g., treatment vs. control)"], ["Paired (dependent) t-test", "Compares means from the same group at two time points (e.g., before vs. after treatment)"], ["One-sample t-test", "Tests whether a sample mean differs from a known/hypothesised value"], ], headers=["Type", "Application"]) add_bullet(doc, "Dental applications: comparing dental material strength, RCT outcomes, bleaching agent efficacy") add_h2(doc, "4.3 F-Test — Analysis of Variance (ANOVA)") add_table_2col(doc, [ ["Use when", "Comparing three or more group means simultaneously"], ["Formula", "F = Between-group variance / Within-group variance"], ["Advantage", "Avoids inflated Type I error from multiple t-tests"], ["Post-hoc", "Tukey, Bonferroni, or Scheffe tests identify which specific groups differ"], ["Applications", "Comparing multiple dental materials; multi-arm clinical trials; periodontal studies"], ], headers=["Aspect", "Detail"]) # ═══════════════════════════════════════════════════════════════════════════════ # 5. NON-PARAMETRIC TESTS # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "5. Non-Parametric Tests") add_body(doc, "Non-parametric tests make no assumptions about the underlying population distribution. They are used when data are ordinal, categorical, or clearly non-normal.") add_h3(doc, "Key Characteristics") add_bullet(doc, "Suitable for ordinal data, ranked data, and small samples") add_bullet(doc, "Less powerful than parametric tests when parametric assumptions are met") add_bullet(doc, "Use median rather than mean as the measure of central tendency") doc.add_paragraph() add_h2(doc, "5.1 Chi-Square Test (χ²)") add_table_2col(doc, [ ["Purpose", "Tests association between two categorical variables"], ["Formula", "χ² = Σ [(O - E)² / E] where O = observed, E = expected frequency"], ["Applications", "Gender vs. oral disease; smoking vs. periodontitis; fluoride exposure vs. dental fluorosis; bruxism vs. tooth wear"], ["Assumption", "Expected frequency in each cell should be ≥ 5"], ], headers=["Aspect", "Detail"]) add_h2(doc, "5.2 Median Test") add_table_2col(doc, [ ["Purpose", "Compares medians of two independent groups"], ["Suitable for", "Ordinal data; small sample studies; clinical satisfaction scores; pain scales"], ["Advantage", "Simple; no normality assumption required"], ["Limitation", "Less powerful than t-test; ignores magnitude of differences above/below median"], ], headers=["Aspect", "Detail"]) # ═══════════════════════════════════════════════════════════════════════════════ # 6. PARAMETRIC vs NON-PARAMETRIC COMPARISON # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "6. Parametric vs Non-Parametric — Quick Comparison") add_table_2col(doc, [ ["Data type", "Continuous (interval/ratio)", "Ordinal, nominal, or non-normal"], ["Distribution", "Normal (Gaussian)", "Any / unknown"], ["Central measure", "Mean", "Median"], ["Sample size", "Larger samples preferred", "Suitable for small samples"], ["Power", "Higher (when assumptions met)", "Lower"], ["Common tests", "t-test, ANOVA, Z-test", "Chi-Square, Median test, Mann-Whitney"], ["Dental example", "Comparing bracket bond strength", "Patient satisfaction scores"], ], headers=["Feature", "Parametric", "Non-Parametric"]) # ═══════════════════════════════════════════════════════════════════════════════ # 7. CHOOSING THE RIGHT TEST # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "7. Selecting the Appropriate Statistical Test") add_body(doc, "The choice of test depends on the research question, data type, number of groups, and whether the data meet parametric assumptions.") add_table_2col(doc, [ ["Compare 2 independent groups (continuous, normal)", "Independent samples t-test"], ["Compare 2 paired/matched groups (continuous, normal)", "Paired t-test"], ["Compare 3+ independent groups (continuous, normal)", "One-way ANOVA (F-test)"], ["Compare 2 groups (large sample, σ known)", "Z-test"], ["Association between two categorical variables", "Chi-Square test"], ["Compare medians of 2 groups (ordinal)", "Median test"], ], headers=["Scenario", "Recommended Test"]) # ═══════════════════════════════════════════════════════════════════════════════ # 8. APPLICATIONS IN DENTISTRY # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "8. Applications in Dentistry") for item in [ "Comparison of fracture resistance of dental materials (ANOVA/t-test)", "Comparison of irrigating solutions in root canal treatment (t-test)", "Comparison of root canal sealers (ANOVA)", "Comparison of bleaching agents (paired t-test)", "Patient satisfaction studies — VAS/NRS pain scores (non-parametric)", "Post-operative pain comparison between techniques (t-test / Mann-Whitney)", "Association of smoking with periodontal disease severity (Chi-Square)", "Comparison of fluoride levels and caries prevalence (Chi-Square)", ]: add_bullet(doc, item) # ═══════════════════════════════════════════════════════════════════════════════ # 9. ADVANTAGES & LIMITATIONS # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "9. Advantages and Limitations") add_h2(doc, "Advantages") for a in [ "Enables scientific, objective decision-making", "Reduces personal bias in interpreting results", "Supports evidence-based dentistry and clinical practice", "Quantifies the probability of observing results by chance", ]: add_bullet(doc, a) add_h2(doc, "Limitations") for l in [ "Applying the wrong test leads to incorrect conclusions", "Parametric tests require strict assumptions — violation invalidates results", "Sample size strongly influences p-values (large samples can produce trivially significant results)", "Statistical significance ≠ clinical significance", "P-value alone does not quantify the magnitude of an effect (effect size needed)", ]: add_bullet(doc, l) # ═══════════════════════════════════════════════════════════════════════════════ # 10. REFERENCES # ═══════════════════════════════════════════════════════════════════════════════ add_h1(doc, "10. References") refs = [ "Soben Peter. Essentials of Public Health Dentistry. 7th ed. New Delhi: Arya (Medi) Publishing House; 2020.", "John J. Textbook of Preventive and Community Dentistry. 3rd ed. CBS Publishers; 2019.", "Mishra P, Singh U, Pandey CM, et al. Application of Student's t-test, ANOVA, and ANCOVA in medical research. Ann Card Anaesth. 2019;22(4):407-411.", "Suresh KP. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. Journal of Human Reproductive Sciences. 2011;4(1):8-11.", "Pandis N. The use of statistics in dental research. American Journal of Orthodontics and Dentofacial Orthopedics. 2011;139(4):555-556.", ] for i, r in enumerate(refs, 1): p = doc.add_paragraph() p.add_run(f"{i}. {r}") p.paragraph_format.space_after = Pt(4) p.paragraph_format.left_indent = Inches(0.3) p.paragraph_format.first_line_indent = Inches(-0.3) # ── Save ────────────────────────────────────────────────────────────────────── out = "/tmp/workspace/summary-doc/Test_of_Significance_Summary.docx" doc.save(out) import os print(f"✅ Saved: {out}") print(f"Size: {os.path.getsize(out):,} bytes") EOF

python3 -c " from docx import Document doc = Document('/tmp/workspace/summary-doc/Test_of_Significance_Summary.docx') count = sum(1 for p in doc.paragraphs if p.text.strip()) tables = len(doc.tables) print(f'Paragraphs with content: {count}') print(f'Tables: {tables}') print('--- Sample headings ---') for p in doc.paragraphs: if p.text.strip() and len(p.text) < 80: print(' ', p.text[:80]) "